Split (1)

train · 15k rows

paper_id stringlengths 19 21	paper_title stringlengths 8 170	paper_abstract stringlengths 8 5.01k	paper_acceptance stringclasses 18 values	meta_review stringlengths 29 10k	label stringclasses 3 values	review_ids list	review_writers list	review_contents list	review_ratings list	review_confidences list	review_reply_tos list
nips_2022_X8mmH03wFlD	Understanding the Failure of Batch Normalization for Transformers in NLP	Batch Normalization (BN) is a core and prevalent technique in accelerating the training of deep neural networks and improving the generalization on Computer Vision (CV) tasks. However, it fails to defend its position in Natural Language Processing (NLP), which is dominated by Layer Normalization (LN). In this paper, w...	Accept	The paper studies the reason why Batch Normalization is not effective in NLP tasks. The authors find that the inconsistency between training and inference leads to the failure. They define Training Inference Discrepancy (TID) to measure the inconsistency and show that BN can obtain better performance when TID is small....	train	[ "LY8v6fUCnUi", "QKnk09ZZnv", "2L3dNc_8gD", "1GGRNny_7lx", "Y0jiNoVtkgu", "HrwmgEeXjEz", "9SdFLPP-PGE", "-XkpWWrYByv" ]	[ "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Sure, we will add the additional results to the revised version. Thanks!", " The additional experimental results should be included in the revised version to make this work more convincing.", " We thank the reviewer for the encouraging and insightful comments. ", " We thank the reviewer for the encouraging ...	[ -1, -1, -1, -1, -1, 6, 6, 5 ]	[ -1, -1, -1, -1, -1, 3, 3, 3 ]	[ "QKnk09ZZnv", "1GGRNny_7lx", "-XkpWWrYByv", "9SdFLPP-PGE", "HrwmgEeXjEz", "nips_2022_X8mmH03wFlD", "nips_2022_X8mmH03wFlD", "nips_2022_X8mmH03wFlD" ]
nips_2022_W-xJXrDB8ik	Unsupervised Visual Representation Learning via Mutual Information Regularized Assignment	This paper proposes Mutual Information Regularized Assignment (MIRA), a pseudo-labeling algorithm for unsupervised representation learning inspired by information maximization. We formulate online pseudo-labeling as an optimization problem to find pseudo-labels that maximize the mutual information between the label and...	Accept	This paper proposes a pseudo-labelling algorithm for unsupervised representation learning inspired by information maximization. The reviewers found that the proposed method is theoretically well grounded and that the authors provide extensive experimentations to demonstrate the validity of their approach. I agree wi...	train	[ "GS-J0q8RzV4", "DI35LWbYtY", "s-IBBepzZME", "oAtkE--MEw9", "oGdYxeQnfvJV", "Zft6wEhMaC", "ji8_QAG4Iz3", "8OgWIwgoxhK", "mdBrnSMOit", "Qhp_i9xk_6U", "zX87lv2njFZ", "oMOKezYed-Z", "IrnaP0iZdVb", "Xfsy5Hmk6C6", "yyOp9cT-ES", "MibC0T8e3I_", "O2icJxbjfBs", "aavw41RbZTD" ]	[ "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We are happy to hear that our responses have been helpful for the understanding of our work. We will incorporate the additional results and feedback, including the limitations, into the next version. For the recommending papers [a, b], we will add discussion on the papers in the revised version.\n\nThank you!\n\n...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 7, 8, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 4 ]	[ "s-IBBepzZME", "Qhp_i9xk_6U", "zX87lv2njFZ", "ji8_QAG4Iz3", "mdBrnSMOit", "IrnaP0iZdVb", "nips_2022_W-xJXrDB8ik", "mdBrnSMOit", "Qhp_i9xk_6U", "aavw41RbZTD", "oMOKezYed-Z", "yyOp9cT-ES", "MibC0T8e3I_", "O2icJxbjfBs", "nips_2022_W-xJXrDB8ik", "nips_2022_W-xJXrDB8ik", "nips_2022_W-xJXr...
nips_2022_um2BxfgkT2_	Pure Transformers are Powerful Graph Learners	We show that standard Transformers without graph-specific modifications can lead to promising results in graph learning both in theory and practice. Given a graph, we simply treat all nodes and edges as independent tokens, augment them with token embeddings, and feed them to a Transformer. With an appropriate choice of...	Accept	The paper applies transformers directly to a graph by treating all nodes and edges in the graph as tokens. The author prove this approach is theoretically at least as expressive as an invariant graph network, which is already more expressive than all message-passing graph neural networks. The approach is simple and int...	test	[ "_wSUrLD4RcM", "cTlAYsgfQVQ", "7XIA1MTSbHG", "kriAHTonRH", "Ny2RLkE3yAd", "QXPw4PGudKI", "AaeKY3Pnu2", "_nj11zepgp1", "2mhIykArnz1", "aZzieG9hMql", "oFnD20SZxT8", "jJq7UJDrv0R", "L5P1o82MDDY", "lylut5jBVny", "L5-55B_Xzrh", "_QNSiesND3_5", "2SUPNaEpJoH", "7RgfdJEhsTG", "rvf1wltR6l...	[ "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author...	[ " Thanks to the author for the detailed reply. Most of my concerns have been addressed. I keep my original rating.\n", " Thank you for the detailed response. Most of my concerns are addressed. Hence I raised my score to 6 (I am actually a 5.5 now). I strongly suggest the authors add all the discussions to the fin...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 7, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 5, 3 ]	[ "QXPw4PGudKI", "Sz-hv6eoMtl", "bC8D4WS0PBHO", "5eDd0-MG5X", "SLEUsI1pVNq", "2SUPNaEpJoH", "aZzieG9hMql", "vNi7da5Aa1EF", "rvf1wltR6lZ", "5eDd0-MG5X", "5eDd0-MG5X", "5eDd0-MG5X", "5eDd0-MG5X", "5eDd0-MG5X", "nips_2022_um2BxfgkT2_", "EByE1osL5uL", "EByE1osL5uL", "lVmW25iPI5Y", "lVm...
nips_2022_Lr2Z85cdvB	Differentiable hierarchical and surrogate gradient search for spiking neural networks	Spiking neural network (SNN) has been viewed as a potential candidate for the next generation of artificial intelligence with appealing characteristics such as sparse computation and inherent temporal dynamics. By adopting architectures of deep artificial neural networks (ANNs), SNNs are achieving competitive performan...	Accept	This paper proposes a new architecture search algorithm for spiking neural networks (SNNs). The key insight is to optimize both the cell and the architecture level of the SNN. Convincing numerical results are provided on image classification tasks (CIFAR10, CIFAR100, and an event-based stereo task). One concern raise...	train	[ "8A5Zx1db9M-", "ZRpPkr5bn5o", "HJ6tN9KC5q8", "XBHjb5P3Bty", "RfBM5ydvewX", "u4AwF9ufDyl", "xLaCRXI0KA", "AvK3nlWU6G4", "EQZxGej2SuS", "6jRJ90u9RYi", "6m0SU06XPa", "zdLed46sH1", "wkJs38wHYJW", "dx7KkE_yqXQ", "zGLcJgDMgfV", "0RzQrCiHb1i", "WW5HtbQ0i4", "CVvSInpg_hh", "GxGNgybmjru",...	[ "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", ...	[ " Thank you for your reply. We understand your concerns for search time cost and power consumption measurement problems in the field of SNN. However, we believe these issues should not influence the main contribution of this work stated in the general response, the reasons are following:\n\n\n**For the issue of sea...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 6, 6, 8 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 3, 5 ]	[ "ZRpPkr5bn5o", "0RzQrCiHb1i", "XBHjb5P3Bty", "RfBM5ydvewX", "u4AwF9ufDyl", "zGLcJgDMgfV", "AvK3nlWU6G4", "dx7KkE_yqXQ", "6jRJ90u9RYi", "6m0SU06XPa", "zdLed46sH1", "wkJs38wHYJW", "xINKCDvGgDo", "YxxibRhimpE", "GxGNgybmjru", "CVvSInpg_hh", "nips_2022_Lr2Z85cdvB", "nips_2022_Lr2Z85cdv...
nips_2022_FRDiimH26Tr	TA-MoE: Topology-Aware Large Scale Mixture-of-Expert Training	Sparsely gated Mixture-of-Expert (MoE) has demonstrated its effectiveness in scaling up deep neural networks to an extreme scale. Despite that numerous efforts have been made to improve the performance of MoE from the model design or system optimization perspective, existing MoE dispatch patterns are still not able to ...	Accept	Mixture-of-Expert (MoE) models have demonstrated a lot of success recently. To further improve upon the existing literature this paper studies MoE routing for different network topologies. This is essentially to deal with the communication overhead of MoE training. The strategy is to add another layer on top for the to...	val	[ "CJjEFXOZzNK", "F6xWNVk_HYP", "L7fg_BjF_lU", "hmVpzhdb8p", "hQsA_W62jWp", "sG7x1zlbhQ1", "E0b_tRA2Ny5", "DnQMqmcbZA", "Ebhq9jKib8R", "OZBQk6it-V7", "xGdY48er99r" ]	[ "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I agree with your point. I will keep my current rating 5 considering all of those. Thanks!", " We appreciate the constructive feedback and valuable suggestions again.\n\nWe apologize that we are currently not able to have experiments with more experts or more updates due to the limitations of the computation re...	[ -1, -1, -1, -1, -1, -1, -1, -1, 5, 5, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 3, 5, 2 ]	[ "F6xWNVk_HYP", "hmVpzhdb8p", "hQsA_W62jWp", "sG7x1zlbhQ1", "E0b_tRA2Ny5", "OZBQk6it-V7", "Ebhq9jKib8R", "xGdY48er99r", "nips_2022_FRDiimH26Tr", "nips_2022_FRDiimH26Tr", "nips_2022_FRDiimH26Tr" ]
nips_2022_4rTN0MmOvi7	DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection	Open-world object detection, as a more general and challenging goal, aims to recognize and localize objects described by arbitrary category names. The recent work GLIP formulates this problem as a grounding problem by concatenating all category names of detection datasets into sentences, which leads to inefficient inte...	Accept	The paper receives overall positive reviews and rebuttal has resolved the reviewer's concerns. Reviewers agree that the paper proposes a simple yet effective approach to enrich language concepts to learn better region-concept alignment for object detection. The approach is supported by solid empirical evidence on the L...	train	[ "bisKCjF7Kg", "9HsBY-fFyYT", "0eT8pCePoSo", "d0Fes_rWLIW", "BR-YqPZcJEK", "9jxWHTM-hxH", "P2V-NZewsdSk", "oDAYdq6SD6", "HZVTm7KSOY", "ke5CqQNASSq", "yZZAUT13S4I", "lSwlXGuae3v" ]	[ "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I appreciate the authors' effort to address my comments, especially evaluating their method using the VILD protocol. This result should be included in the final version (at least in the supplementary). I still believe I was right in my original assessment about the advantages and disadvantages of the proposed met...	[ -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 4, 5, 5, 5 ]	[ "9HsBY-fFyYT", "lSwlXGuae3v", "yZZAUT13S4I", "BR-YqPZcJEK", "ke5CqQNASSq", "P2V-NZewsdSk", "HZVTm7KSOY", "nips_2022_4rTN0MmOvi7", "nips_2022_4rTN0MmOvi7", "nips_2022_4rTN0MmOvi7", "nips_2022_4rTN0MmOvi7", "nips_2022_4rTN0MmOvi7" ]
nips_2022_Wo1HF2wWNZb	On the Identifiability of Nonlinear ICA: Sparsity and Beyond	Nonlinear independent component analysis (ICA) aims to recover the underlying independent latent sources from their observable nonlinear mixtures. How to make the nonlinear ICA model identifiable up to certain trivial indeterminacies is a long-standing problem in unsupervised learning. Recent breakthroughs reformulate ...	Accept	Strong paper with all reviewers arguing for acceptance. Only minor concerns from the reviewers were on whether the preconditions required for the theorems in the paper were likely to hold in practice. This was discussed thoughtfully by the author response, including a new appendix section.	train	[ "2-FK49RYgwHH", "yhBR3ZY6yt", "RhNmxm9pCVn", "YyRX0HGx4L", "CQSSofLkVV9", "C1_sZVQAnIso", "PClA-HpXzo4", "skLa7x0sa0V", "MERRuAam3oB" ]	[ "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We greatly appreciate the reviewer’s time, encouraging comments, and constructive suggestions. Our responses are provided below.\n\nQ1: What would happen if the ground truth data were to violate the assumptions but the method was run anyway?\n\nA1: Thanks for the great question. In the ablation study, ass...	[ -1, -1, -1, -1, -1, -1, 7, 7, 8 ]	[ -1, -1, -1, -1, -1, -1, 3, 4, 2 ]	[ "MERRuAam3oB", "RhNmxm9pCVn", "YyRX0HGx4L", "skLa7x0sa0V", "C1_sZVQAnIso", "PClA-HpXzo4", "nips_2022_Wo1HF2wWNZb", "nips_2022_Wo1HF2wWNZb", "nips_2022_Wo1HF2wWNZb" ]
nips_2022_nRcyGtY2kBC	Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation	Consider the problem of improving the estimation of conditional average treatment effects (CATE) for a target domain of interest by leveraging related information from a source domain with a different feature space. This heterogeneous transfer learning problem for CATE estimation is ubiquitous in areas such as healthca...	Accept	The paper studies methods for estimating conditional average treatment effects (CATE) under a shift in domain where source and target feature spaces are heterogenous. It is assumed that the (respective) CATEs in both source and target domains are identifiable through ignorability and overlap. No formal assumptions are ...	train	[ "yn0PVHkgsy0", "h9bRlCbPoy0", "O-0n4XN8rPZ", "nmd8lSbCW1f", "XQCjMkoH9j5", "F3AUXTpebPx", "zJq-nSyvU2q", "l8x35inBJxO", "ZvZOMrUFLx", "CTevtWAcbFw", "793DtsPMODP", "fjE682UB8iA", "xiBcPK4JR7n", "CEZuhRLf1OL" ]	[ "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for the thoughtful responses. After reading this response, along with the other reviews and author responses, I retain my recommendation of acceptance.\n\nThank you for your detailed thoughts on the specific topics and extensions which I mentioned in the original review. While I find these specific topi...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 2, 5, 5 ]	[ "793DtsPMODP", "ZvZOMrUFLx", "F3AUXTpebPx", "CEZuhRLf1OL", "CEZuhRLf1OL", "CEZuhRLf1OL", "xiBcPK4JR7n", "xiBcPK4JR7n", "xiBcPK4JR7n", "fjE682UB8iA", "fjE682UB8iA", "nips_2022_nRcyGtY2kBC", "nips_2022_nRcyGtY2kBC", "nips_2022_nRcyGtY2kBC" ]
nips_2022_HH_jBD2ObPq	BR-SNIS: Bias Reduced Self-Normalized Importance Sampling	Importance Sampling (IS) is a method for approximating expectations with respect to a target distribution using independent samples from a proposal distribution and the associated to importance weights. In many cases, the target distribution is known up to a normalization constant and self-normalized IS (SNIS) is then ...	Accept	Importance sampling requires the knowledge of the normalization constant of the distribution to be sampled from. SNIS (Self-Normalized Importance Sampling) does not, but is biased. The authors study methods to reduce the bias in SNIS: BR-SNIS. They prove error bounds for this method (Theorems 3 and 4). The reviewers a...	train	[ "wavCzxsfvl4", "EQwzAiPQAAN", "DuvDaNSZ3xN", "61QO8X4_THbK", "CfUmSbW2Wlo", "dd8uMhGIBnR", "X1D3bKfnrd", "m-qK1gHzti", "pj9ziYysbcd", "MMWA4ILzbjr", "bzo3Fc9xll_", "IKWrFgrbYyd", "GOaeoeTV_r", "aQ5tpiNkMZ", "CtIPnFED4nh", "2mnaykexEas", "37wQOx_nvV9", "CuKt6-Er8AtW", "AhxI7Sm8-eX...	[ "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", ...	[ " Thanks for your feedback. Following your suggestion, we have now added—a short version of—this discussion to Section 2.3. ", " Thanks a lot for this remark. We have added a reference to the work you mention at the point where we introduce the induced Markov kernel in question. ", " Thank you for the detailed ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 5, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 4, 3 ]	[ "CfUmSbW2Wlo", "DuvDaNSZ3xN", "CuKt6-Er8AtW", "AhxI7Sm8-eX", "GOaeoeTV_r", "nips_2022_HH_jBD2ObPq", "bzo3Fc9xll_", "nips_2022_HH_jBD2ObPq", "0rtg_rI1XL8", "0rtg_rI1XL8", "0rtg_rI1XL8", "-5y87WueO2I", "-5y87WueO2I", "e5urouJ0bep", "xhjej3dZayO", "xhjej3dZayO", "xhjej3dZayO", "xhjej3...
nips_2022_xWvI9z37Xd	Where to Pay Attention in Sparse Training for Feature Selection?	A new line of research for feature selection based on neural networks has recently emerged. Despite its superiority to classical methods, it requires many training iterations to converge and detect the informative features. For datasets with a large number of samples or a very high dimensional feature space, the comput...	Accept	After the rebuttal and discussion, all reviewers recommend acceptance of this paper to some degree. The paper has benefited from a careful review by reviewer U347 and additional experiments and clarifications performed by the authors in response to the reviewer's concerns. All reviewers noted that the paper is clearly ...	train	[ "YeDk9PYcxBe", "XLVDXqVMhrM", "xo6xn505gK", "T67jspSf5xQ", "DgxsZR_4Go", "LafUzOFqcn0", "745wHHn4fFx", "XyxzqA7JvrO", "uzkDpDSpCCx", "KtXBKaPDhg", "wws3d2ibO6U", "B_byu8dozje", "zHQvPF8B0Kw", "xXIfanKOdea", "PyKvQ0rdeQf", "mRuf-f_3FGa" ]	[ "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you. We appreciate the reviewer’s time in checking our response and the constructive suggestions. We will illustrate the overlapping features on Madelon in the final version of the paper.", " Thanks for your reply,\n\n> We did follow QS in adding noise to the input features. Kindly find this information i...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 8, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 4, 4, 4 ]	[ "XLVDXqVMhrM", "LafUzOFqcn0", "uzkDpDSpCCx", "nips_2022_xWvI9z37Xd", "nips_2022_xWvI9z37Xd", "mRuf-f_3FGa", "PyKvQ0rdeQf", "xXIfanKOdea", "KtXBKaPDhg", "wws3d2ibO6U", "B_byu8dozje", "zHQvPF8B0Kw", "nips_2022_xWvI9z37Xd", "nips_2022_xWvI9z37Xd", "nips_2022_xWvI9z37Xd", "nips_2022_xWvI9z...
nips_2022_DmT862YAieY	A Continuous Time Framework for Discrete Denoising Models	We provide the first complete continuous time framework for denoising diffusion models of discrete data. This is achieved by formulating the forward noising process and corresponding reverse time generative process as Continuous Time Markov Chains (CTMCs). The model can be efficiently trained using a continuous time ve...	Accept	The work proposes a continuous-time generalization of diffusion models on a discrete space. The description uses continuous-time Markov chain (CTMC), in parallel to the existing stochastic differential equation description for continuous space. Reverse CTMC and modeling and ELBO objective are described. Some practical ...	train	[ "uvKKjIqa1WR", "O_XreQ-OaS", "YW9tb7hXj52", "ksywP7FVRsJ", "5NiFJvebzl", "l2gRVMcCFlu", "luethdqMXyA", "7wtqXZh4lFd", "ZFrmIaGePVo", "AhqZsm6VcVG", "ndADo_yUjpu", "-bjFx7gK9Cn", "25eVeSCfLWV", "nPAWYINL9vL", "uuWHL-pBNi", "zPSY2_P87e9", "6Dqa_9V8Dr" ]	[ "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for the answer. I will keep my recommendation for acceptance. ", " I thank the authors for their detailed response. All of my questions have been answered and I raised my score accordingly.", " Thanks for the clear response. I have raised my score.", " I would like to thank the authors for their d...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 7, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 4 ]	[ "25eVeSCfLWV", "5NiFJvebzl", "7wtqXZh4lFd", "AhqZsm6VcVG", "l2gRVMcCFlu", "luethdqMXyA", "6Dqa_9V8Dr", "ZFrmIaGePVo", "zPSY2_P87e9", "ndADo_yUjpu", "-bjFx7gK9Cn", "nPAWYINL9vL", "uuWHL-pBNi", "nips_2022_DmT862YAieY", "nips_2022_DmT862YAieY", "nips_2022_DmT862YAieY", "nips_2022_DmT862...
nips_2022_XYDXL9_2P4	AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning	Fine-tuning large pre-trained language models on downstream tasks is apt to suffer from overfitting when limited training data is available. While dropout proves to be an effective antidote by randomly dropping a proportion of units, existing research has not examined its effect on the self-attention mechanism. In this...	Accept	The paper proposes a method AD-DROP to drop attention weights in a network to alleviate overfitting. It randomly sample a set of token positions with respect to attribution score calculated in first pass. The authors provide a variety of experiments on multiple tasks (SNLI, NER, MT, etc.) showing effectiveness compa...	test	[ "xwyLjAWMaa", "okScoyGHsQW", "zEJHTnRVSdD", "XrG9IXWr-2", "gDRcp9i4NrB", "I0k1uxLJVub", "OPQvox0oKzf", "YxpMx8-ni4_", "b8OPYeIVE_", "NHWgoimpv_", "9Idjo23DzV", "pfJXd9jP-GU", "4EEOma_0WoM", "J4mKzzAs1M", "Z6bUS5ZU5LC" ]	[ "author", "official_reviewer", "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for the question. We conduct the MT experiments following the official colab (https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/translation.ipynb) with the pretrained OPUS-MT model, and report the BLEU results on the test set after five epochs. We found that the BLEU sco...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 6, 4, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 4, 3 ]	[ "okScoyGHsQW", "YxpMx8-ni4_", "J4mKzzAs1M", "b8OPYeIVE_", "OPQvox0oKzf", "NHWgoimpv_", "Z6bUS5ZU5LC", "J4mKzzAs1M", "4EEOma_0WoM", "pfJXd9jP-GU", "nips_2022_XYDXL9_2P4", "nips_2022_XYDXL9_2P4", "nips_2022_XYDXL9_2P4", "nips_2022_XYDXL9_2P4", "nips_2022_XYDXL9_2P4" ]
nips_2022_4F7vp67j79I	Verification and search algorithms for causal DAGs	We study two problems related to recovering causal graphs from interventional data: (i) $\textit{verification}$, where the task is to check if a purported causal graph is correct, and (ii) $\textit{search}$, where the task is to recover the correct causal graph. For both, we wish to minimize the number of interventions...	Accept	This paper's reviews as it stands are divergent. The scores are 7, 5 and 4. The paper has seen discussion between reviewers with negative opinion and the authors. One reviewer who engaged in discussion revised the score up by 1. The most unfavorable reviewer's main issue was lack of empirical evaluations comparing the...	train	[ "SxO5-bM7tQ", "Qr96OFz9Ang", "TiqNDYnG7i3", "qgyOBjThGu", "-fcHONNif-a", "hfUNvb8ioE-", "51ogZ87ya44", "N6-h0Qo7PAO", "8bTvJAoapPo", "hdhr_Tz6IC_", "o9fGIVb96de", "iG6l5Xx8cYO", "tP_2hC-qPwE", "eliRm7ZFsWf" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you so much for your time. We really appreciate your responses and are glad that we could have this discussion! Please refer to the following for our responses.\n\n### Comment 1: Theorem 9 via Lemma 7\n\nOn necessity: While your statement is true, it is insufficient as a proof for the necessity. For examp...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 7, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 2, 2, 4 ]	[ "Qr96OFz9Ang", "TiqNDYnG7i3", "qgyOBjThGu", "eliRm7ZFsWf", "eliRm7ZFsWf", "eliRm7ZFsWf", "eliRm7ZFsWf", "iG6l5Xx8cYO", "iG6l5Xx8cYO", "tP_2hC-qPwE", "tP_2hC-qPwE", "nips_2022_4F7vp67j79I", "nips_2022_4F7vp67j79I", "nips_2022_4F7vp67j79I" ]
nips_2022_yoLGaLPEPo_	Hyperbolic Feature Augmentation via Distribution Estimation and Infinite Sampling on Manifolds	Learning in hyperbolic spaces has attracted growing attention recently, owing to their capabilities in capturing hierarchical structures of data. However, existing learning algorithms in the hyperbolic space tend to overfit when limited data is given. In this paper, we propose a hyperbolic feature augmentation method t...	Accept	This paper attempts to improve learning in hyperbolic space under limited data (few shot setting). In this regards, the authors propose a hyperbolic feature augmentation method to circumvent overfitting. Furthermore, as optimizing using a large number of sampled data can be expensive, the paper proposes an upper bound ...	val	[ "xdE9kQqaHJ", "KGDQ82VVKeO", "k3cCMmMqm8u", "W5Zcp3Umey8", "FgjmSzaxsOY", "oJCr6pvKUO2", "auBSRmeYId4", "h0YznPY1Ti7", "V5wUN21Wr_", "ooRuOV31f2x", "C-ICPik5ySd", "AC6wB_38xPf" ]	[ "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewer KDoT,\n\nWe thank you for the review time and valuable comments. We have provided corresponding responses and results, which we believe have covered your concerns. We hope to further discuss with you whether or not your concerns have been addressed. If yes, we would appreciate it if you could improv...	[ -1, -1, -1, -1, -1, -1, -1, -1, 8, 4, 5, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 5, 4, 3, 4 ]	[ "ooRuOV31f2x", "C-ICPik5ySd", "AC6wB_38xPf", "C-ICPik5ySd", "ooRuOV31f2x", "ooRuOV31f2x", "V5wUN21Wr_", "V5wUN21Wr_", "nips_2022_yoLGaLPEPo_", "nips_2022_yoLGaLPEPo_", "nips_2022_yoLGaLPEPo_", "nips_2022_yoLGaLPEPo_" ]
nips_2022_c4o5oHg32CY	TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers	Mixup is a commonly adopted data augmentation technique for image classification. Recent advances in mixup methods primarily focus on mixing based on saliency. However, many saliency detectors require intense computation and are especially burdensome for parameter-heavy transformer models. To this end, we propose Token...	Accept	The paper is about speeding up the saliency computation used in gradient based mixup algorithms(Puzzlemix, Co-mixup, etc). The authors propose employing the attention layer output of the transformer to replace the expensive saliency computation. Since focus of the the paper is on improving the speed and accuracy aspe...	test	[ "bcd4-8sNmzk", "Ez3QBLlS5GG", "DWt6W54RUcb", "0Ek_5Umyic7", "8nDPPRXyLwn", "Lvhc6dZrY_8", "Btscd3gRP7G", "sHLKpL7c7V9", "dkX4KprRGN7", "kk9v7etWaum", "aeaxyCT2hk" ]	[ "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for the detailed clarifications and additional experiments.\n\nThe response addresses most of my concerns. Especially, the response to Q2 seems to show an interesting difference between gradient-based methods and attention-based methods. Also, the response to Q3 seems fair enough.\n\nBased on the response,...	[ -1, -1, -1, -1, -1, -1, -1, 7, 7, 5, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, 3, 4, 2, 5 ]	[ "0Ek_5Umyic7", "nips_2022_c4o5oHg32CY", "aeaxyCT2hk", "kk9v7etWaum", "nips_2022_c4o5oHg32CY", "dkX4KprRGN7", "sHLKpL7c7V9", "nips_2022_c4o5oHg32CY", "nips_2022_c4o5oHg32CY", "nips_2022_c4o5oHg32CY", "nips_2022_c4o5oHg32CY" ]
nips_2022_Jw34v_84m2b	IM-Loss: Information Maximization Loss for Spiking Neural Networks	Spiking Neural Network (SNN), recognized as a type of biologically plausible architecture, has recently drawn much research attention. It transmits information by $0/1$ spikes. This bio-mimetic mechanism of SNN demonstrates extreme energy efficiency since it avoids any multiplications on neuromorphic hardware. However,...	Accept	This paper proposes a novel loss for training a spiking neural network that mitigates errors due to quantization. All reviewers agreed that the contributions of this paper were above the acceptance threshold.	train	[ "KfWMpN7C2oi", "3-I7LZjAjik", "PdSoinzYLbG", "TE8RbsU9iJS", "U9cb7cCZH-Y", "SF8BTqRXGSa", "20G-bbdeD9y", "sps0obxIVRK", "KN1Zj48w4AK", "vGWUB9a7mhX", "bT8n5SzM9Xr", "-qiLJrJmmUk", "-7Wn4C4Qlw", "xQDloNpEWq5", "CWllSUj-z6n", "VooeZWLQioH", "Ri6eIzCfVlq", "0DIxdIhAvIM", "5q0wTMn4OO...	[ "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", ...	[ " Thanks for your timely reply. We are very grateful to meet such a responsible reviewer. We have taken your suggestion seriously and added an extra section to discuss the sparsity and efficiency cost in the revised version (see line 300-308 in revision). We'd like to know if you have any other questions. We would ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 5, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 5 ]	[ "3-I7LZjAjik", "PdSoinzYLbG", "TE8RbsU9iJS", "Ri6eIzCfVlq", "SF8BTqRXGSa", "20G-bbdeD9y", "vGWUB9a7mhX", "KN1Zj48w4AK", "VooeZWLQioH", "-7Wn4C4Qlw", "nips_2022_Jw34v_84m2b", "u72L0FBrqi3", "u72L0FBrqi3", "u72L0FBrqi3", "u72L0FBrqi3", "u72L0FBrqi3", "nIKOaMSCJX8", "nIKOaMSCJX8", "...
nips_2022_70bBDacSpNn	Operator-Discretized Representation for Temporal Neural Networks	This paper proposes a new representation of artificial neural networks to efficiently track their temporal dynamics as sequences of operator-discretized events. Our approach takes advantage of diagrammatic notions in category theory and operator algebra, which are known mathematical frameworks to abstract and discretiz...	Reject	Reviewers agree that manuscript presents a fresh attempt, but also that the manuscript is lacking in several aspects. The writing has a lot of room for improvement and not suitable for the NeurIPS community. It's neuroscientific claims are controversial, or relies on non-mainstream arguments without appropriate justifi...	train	[ "3ajulTFQFJ6", "mYYXLBUGwB", "rEhu_OYZXW", "Ykgulll0q-x", "pObWdspEpz1", "s3JZv3E8hvS", "ySFuI50QNk5", "KZKwlttBnh9", "D62fmi35e2Q", "OemuTBnfJt", "poGT58ZLp0V", "xXLca0vOeuA", "0PA5i0usmj" ]	[ "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for your clarification. I see your point better. We will update it with this focus. If you have other points for us to clarify, please let us know ASAP. ", " First of all, as the title says, our paper is on a new representation (or language in your wording) for temporal neural networks. We do not have...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 3, 3 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 2, 2, 1, 2 ]	[ "Ykgulll0q-x", "rEhu_OYZXW", "D62fmi35e2Q", "s3JZv3E8hvS", "poGT58ZLp0V", "0PA5i0usmj", "xXLca0vOeuA", "poGT58ZLp0V", "OemuTBnfJt", "nips_2022_70bBDacSpNn", "nips_2022_70bBDacSpNn", "nips_2022_70bBDacSpNn", "nips_2022_70bBDacSpNn" ]
nips_2022__iXQPM6AsQD	Could Giant Pre-trained Image Models Extract Universal Representations?	Frozen pretrained models have become a viable alternative to the pretraining-then-finetuning paradigm for transfer learning. However, with frozen models there are relatively few parameters available for adapting to downstream tasks, which is problematic in computer vision where tasks vary significantly in input/output ...	Accept	This paper presents a study of how well pre-trained and frozen large models work across several downstream computer vision tasks. The paper initially received mixed reviews with two of them being borderline accept and one borderline reject. The reviewers shared their concerns about the novelty of the investigation and ...	train	[ "2_Ywy2vvMA", "fuola9hp4KV", "u70Efa5UZS", "V5TtjoDMxex", "rlHsA_KHNza", "3yWjJC6xgSw", "6xX6p-lHsyUP", "rppWfLBpEQQ", "oA2ypPb1gg", "Zn5LitvYKpc", "A1sBSDPtWVw", "5pJM3jGEC_b" ]	[ "author", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you again for your valuable and thoughtful comments. Do you have more comments or questions about this submission and our response? We can prepare answers or more results for them.", " We sincerely thank the reviewer for the constructive comments, which help us improve the paper. We would add the results ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 4, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 5 ]	[ "6xX6p-lHsyUP", "rlHsA_KHNza", "V5TtjoDMxex", "3yWjJC6xgSw", "rppWfLBpEQQ", "5pJM3jGEC_b", "A1sBSDPtWVw", "Zn5LitvYKpc", "nips_2022__iXQPM6AsQD", "nips_2022__iXQPM6AsQD", "nips_2022__iXQPM6AsQD", "nips_2022__iXQPM6AsQD" ]
nips_2022_IlYS1pLa9y	Searching for Better Spatio-temporal Alignment in Few-Shot Action Recognition	Spatio-Temporal feature matching and alignment are essential for few-shot action recognition as they determine the coherence and effectiveness of the temporal patterns. Nevertheless, this process could be not reliable, especially when dealing with complex video scenarios. In this paper, we propose to improve the perfor...	Accept	All three reviewers lean towards the acceptance of the paper. The reviewers believe the rebuttal has addressed their concerns. The AC recommends acceptance of the paper, and suggest the authors to include the materials and the discussion they promised in the rebuttal in the final version of the paper.	test	[ "KdE3VJQ7jly", "xGWamRe-1XK", "CkXbZpyPmnN", "3JX4sDyDwElX", "pYZuMd8u8ys4", "6bG4bp3mTyof", "cQAH6Ym3vwo", "ocAphxfGLlz", "ARTI-w73XDm", "h1tgp9aGXs", "NFKorCJFun4", "dgc0Adr96_K", "zP1n3dVwbo", "KkAFflc2KBx", "uUw3NH4O7Yk", "GXq9UzQR2Ix", "gIVItTuSEb1", "XGAPSWZyRnC", "3JumZEX0...	[ "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", ...	[ " I appreciate the effort made by the authors to provide additional experimental results. The response addressed all of my concerns. Therefore, I raise my rating to weak accept.", " Dear reviewer 4Tcp:\n\nWe sincerely thank you for your careful reviews and insightful comments. We have tried our best to respond to...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4 ]	[ "GXq9UzQR2Ix", "2l4RgRUa9e5", "3JX4sDyDwElX", "pYZuMd8u8ys4", "ocAphxfGLlz", "ARTI-w73XDm", "h1tgp9aGXs", "KkAFflc2KBx", "uUw3NH4O7Yk", "XGAPSWZyRnC", "2l4RgRUa9e5", "3JumZEX0Pk", "vdveXOdNY5", "3JumZEX0Pk", "3JumZEX0Pk", "2l4RgRUa9e5", "vdveXOdNY5", "3JumZEX0Pk", "nips_2022_IlYS...
nips_2022_dIUQ5haSOI	Relation-Constrained Decoding for Text Generation	The dominant paradigm for neural text generation nowadays is seq2seq learning with large-scale pretrained language models. However, it is usually difficult to manually constrain the generation process of these models. Prior studies have introduced Lexically Constrained Decoding (LCD) to ensure the presence of pre-speci...	Accept	The paper describes a model for text generation, based on target dependency relations that should be in the output. The word-level output probabilities are modified to increase the likelihood of generating words that match the target relation. Evaluation is performed on several datasets, formulating the task as text ge...	train	[ "-ZKlSYokhP", "GdqK_1kdGG2", "Yalb0vZsy7A", "hSgXY5q8pPja", "6LcqpiyIJ0x", "Cy_8tZ2E44", "Z2ocI_woalM", "a9Ymn4yK3Rf", "GZho3ImOGUh", "lYqR0LrN364", "1gE7hJE7VJg", "IZYnIEVJgfI", "mofdM99Lvk_", "2kmjN6Byaiz", "szJ7d0e99Sm", "Rgv-zMfw3nP", "OjxYfxOZGao" ]	[ "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We really appreciate all the reviewers for their efforts in reviewing our paper. We believe that we have responded to most of the concerns below. Since the discussion deadline is approaching, here we'd like to briefly summarize the updates we have made to the revised version of the paper:\n\n- We have clarified t...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 7, 4, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 3, 5 ]	[ "nips_2022_dIUQ5haSOI", "OjxYfxOZGao", "nips_2022_dIUQ5haSOI", "OjxYfxOZGao", "Rgv-zMfw3nP", "nips_2022_dIUQ5haSOI", "Rgv-zMfw3nP", "Rgv-zMfw3nP", "Rgv-zMfw3nP", "2kmjN6Byaiz", "OjxYfxOZGao", "OjxYfxOZGao", "szJ7d0e99Sm", "nips_2022_dIUQ5haSOI", "nips_2022_dIUQ5haSOI", "nips_2022_dIUQ5...
nips_2022_hyc27bDixNR	Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation	Few-shot class-incremental learning (FSCIL) is designed to incrementally recognize novel classes with only few training samples after the (pre-)training on base classes with sufficient samples, which focuses on both base-class performance and novel-class generalization. A well known modification to the base-class train...	Accept	This work studied the few-shot class-incremental learning in the margin-based classification. It presented a deeper analysis about the dilemma between the base-class and novel class performance, from the perspective of positive and negative patterns corresponding to positive and negative margins. Although this dilemma ...	train	[ "Fmxt1UeJXVT", "GR31jK81Ulv", "CL7YUAV-Jak", "e2_a-WLTRDx", "7YgcTK6hBeY", "pG6U43rH0rb", "pa9SPLtWJx", "6yRPDVcBmyt", "C1ewEXcbgRg", "Eln98Q3D1oM", "y_gtP-LhC3L", "x__z3CGl4xd", "WD35hHxuo3J", "xcXJKxRrtce" ]	[ "author", "author", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you very much for reading our response! May I know if our response have addressed your questions? Please feel free to let us know if you have any question. We are very much looking forward to having the opportunity to discuss with you.", " Thank you very much for reading our response! May I know if our re...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 5, 5 ]	[ "x__z3CGl4xd", "xcXJKxRrtce", "pG6U43rH0rb", "7YgcTK6hBeY", "Eln98Q3D1oM", "pa9SPLtWJx", "xcXJKxRrtce", "C1ewEXcbgRg", "Eln98Q3D1oM", "WD35hHxuo3J", "x__z3CGl4xd", "nips_2022_hyc27bDixNR", "nips_2022_hyc27bDixNR", "nips_2022_hyc27bDixNR" ]
nips_2022_Setj8nJ-YB8	Zeroth-Order Negative Curvature Finding: Escaping Saddle Points without Gradients	We consider escaping saddle points of nonconvex problems where only the function evaluations can be accessed. Although a variety of works have been proposed, the majority of them require either second or first-order information, and only a few of them have exploited zeroth-order methods, particularly the technique of n...	Accept	This paper designs new algorithms for finding second order stationary points using only function value queries (0th order information). The main novelty is in designing two approaches for negative curvature finding. The new subroutines can be used in a wide range of algorithms for finding second order stationary points...	val	[ "6OUK9F20sbP", "SQOcKIb-xzi", "RxeUxU3kCVk", "C7DYHQnGL0W", "OG1B0EdZoAn", "DCoNl-jm3gA9", "DKWLtMkkfhm", "8f5c7AtqXGf", "Ejb36Zj9hO0", "_kbIiP8150", "4L3td_u4RPN", "aeWa9D645tN", "jT8eA9ylkr8", "LjReTs9H3M5", "f8_yqDtieT_", "fdoOzmEenrB", "UvLwbREd76n", "cQ1Hudikrp" ]	[ "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for reading our response and thanks for raising the score.", " Thanks again for your further feedback and thanks for raising the score!", " Thank you for the detailed replies to my questions and comments. I especially appreciate the detailed explanation on iteration complexity and the addition of empir...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 6, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 3, 4 ]	[ "C7DYHQnGL0W", "RxeUxU3kCVk", "_kbIiP8150", "jT8eA9ylkr8", "DCoNl-jm3gA9", "DKWLtMkkfhm", "4L3td_u4RPN", "nips_2022_Setj8nJ-YB8", "_kbIiP8150", "f8_yqDtieT_", "aeWa9D645tN", "cQ1Hudikrp", "UvLwbREd76n", "fdoOzmEenrB", "nips_2022_Setj8nJ-YB8", "nips_2022_Setj8nJ-YB8", "nips_2022_Setj8...
nips_2022_JRAlT8ZstmH	Latency-aware Spatial-wise Dynamic Networks	Spatial-wise dynamic convolution has become a promising approach to improving the inference efficiency of deep networks. By allocating more computation to the most informative pixels, such an adaptive inference paradigm reduces the spatial redundancy in image features and saves a considerable amount of unnecessary comp...	Accept	The paper proposes latency-aware spatial-wise dynamic neural networks under the guidance of a latency prediction mode. reviewers arrived at a consensus to accept the paper.	train	[ "gR79srqyTk", "mkiq_dx3bGh", "EZfOuGqbanv", "iNvtjdMj8ra", "Xl3cgaDHkHY", "_znD4W2JmHq", "mA_3B-eQ1oo", "ZeRzlERlxl4", "iA9EoCGz0K", "pGhm1vB_uEY", "7fWrBv6caS", "TtnqGqrV1nv", "oq7oqhMKml", "MgtixVVmfpp", "SqzOlA5e9g", "L1DNLhxnDdK", "yGC04AvrBF4", "E5r8zz_pc-y", "OG1T3DO0pWf" ]	[ "author", "author", "official_reviewer", "author", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewer,\n\nWe wanted to reach out to see if our most recent reply and paper revisions have addressed the concerns. With only ~17 hours left in the rebuttal period, we were hoping that the reviewer could confirm that the revisions have addressed the concerns about the problem statement, further clarify the ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 6, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 3 ]	[ "OG1T3DO0pWf", "EZfOuGqbanv", "L1DNLhxnDdK", "ZeRzlERlxl4", "_znD4W2JmHq", "oq7oqhMKml", "ZeRzlERlxl4", "TtnqGqrV1nv", "nips_2022_JRAlT8ZstmH", "OG1T3DO0pWf", "OG1T3DO0pWf", "OG1T3DO0pWf", "E5r8zz_pc-y", "yGC04AvrBF4", "yGC04AvrBF4", "yGC04AvrBF4", "nips_2022_JRAlT8ZstmH", "nips_20...
nips_2022_sexfswCc7B	Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation	While large-scale neural language models, such as GPT2 and BART, have achieved impressive results on various text generation tasks, they tend to get stuck in undesirable sentence-level loops with maximization-based decoding algorithms (\textit{e.g.}, greedy search). This phenomenon is counter-intuitive since there are ...	Accept	This paper investigates the source of repetition in text generation from a language model and presents a training method to mitigate this problem. Their experiments show the proposed method not only reduces repetition, but also improves generation quality. I think this is a good paper. All reviewers agreed with me so I...	train	[ "nIGNs_iSwAd", "hXN3tX2Ynat", "ItGMbRDqQUL", "CAGIFK8EsKj", "xLIJRg4uXfc", "ZjFAghnwJvH", "szhBPP31hZ", "2kas10y2IOs", "IAquLGyuuF3" ]	[ "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Reviewers have addressed all questions I had. I have increased the rating.", " We thank the reviewers for their insightful and constructive reviews. We first provide responses to several points raised by multiple reviewers. Responses to individual reviewers are provided below. **The Appendix that contains addit...	[ -1, -1, -1, -1, -1, -1, 6, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, 3, 4, 4 ]	[ "CAGIFK8EsKj", "nips_2022_sexfswCc7B", "szhBPP31hZ", "xLIJRg4uXfc", "2kas10y2IOs", "IAquLGyuuF3", "nips_2022_sexfswCc7B", "nips_2022_sexfswCc7B", "nips_2022_sexfswCc7B" ]
nips_2022_Y6A4-R_Hgsw	Toward a realistic model of speech processing in the brain with self-supervised learning	Several deep neural networks have recently been shown to generate activations similar to those of the brain in response to the same input. These algorithms, however, remain largely implausible: they require (1) extraordinarily large amounts of data, (2) unobtainable supervised labels, (3) textual rather than raw sensor...	Accept	This paper compares learned self-supervised speech representations to brain fMRI representations for more than 400 subjects speaking English, French, and Mandarin. Through the rebuttal period, the authors and reviewers interacted extensively to discuss the contribution, results, and analysis provided in the paper. Most...	train	[ "J3Zq1tZktp", "Vy8rBhMEgVf", "i_Bce2-0BMy", "eu-4WyxfUuS", "Ky7cHltP8dG", "4Xmysa_vpG", "fX52mL4Rqcw", "Nf_1GjZCFmG", "pYX72_a132T", "ayqpTPN2Wy4", "nGGiIj1CSz", "s8Emrgt3HY", "sbwaY_CFz5", "7C2odCAKXdn", "y9mJfbXz522", "vMPCQiMFbiT", "j_CycJwcqXK", "Vi_oPIs20Si", "0bmDGO6cagc", ...	[ "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", ...	[ " Thank you for your response. I am impressed by the new analyses and thorough responses made by the authors and would like to increase my score by 1 more point. However, I urge the authors to include some of the discussion we had here in the paper especially regarding the perils of over-interpretation like in my s...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 5, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 5, 2 ]	[ "Vy8rBhMEgVf", "i_Bce2-0BMy", "xZaau6dfjNH", "Ky7cHltP8dG", "pYX72_a132T", "pYX72_a132T", "Nf_1GjZCFmG", "pYX72_a132T", "ayqpTPN2Wy4", "xZaau6dfjNH", "xZaau6dfjNH", "xZaau6dfjNH", "IYqZ2Q_T__w", "HAI7Bi4lwH", "7f3bddenKKb", "j_CycJwcqXK", "Vi_oPIs20Si", "0bmDGO6cagc", "xZaau6dfjN...
nips_2022_DTD9BRDWtkn	Multi-layer State Evolution Under Random Convolutional Design	Signal recovery under generative neural network priors has emerged as a promising direction in statistical inference and computational imaging. Theoretical analysis of reconstruction algorithms under generative priors is, however, challenging. For generative priors with fully connected layers and Gaussian i.i.d. weight...	Accept	This paper considers finding an input vector from multi-layer noisy measurements. This can alternatively be thought of as finding the latent code of generative models. The authors analyze the state evolution of a multi-layer multi-layer approximate message passing algorithm. The main technical idea is relating random c...	train	[ "don2tz17IAI", "VCe62k-m-K", "L1f-gF-5U3L", "ojiiK5o5Hms", "_ztjKd1Fl1O", "AznkeTgold3", "j3lMC3IWERC", "2_rVZPIxhP-", "dl3eETxOugb", "DXkRxlQbhM", "aA3pvkp3dmL", "Uoa6C9u-XaI", "AA-0lmAlSY9", "2zPFigHuL_v" ]	[ "official_reviewer", "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I have increased my score in terms of the fairness between the other papers I reviewed. ", " Concerning the insights our work can bring for trained networks: we will add our thoughts in the discussion of the paper. There are several trained settings for which the same state evolution could apply and that we are...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 6, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 1, 4 ]	[ "_ztjKd1Fl1O", "ojiiK5o5Hms", "dl3eETxOugb", "DXkRxlQbhM", "j3lMC3IWERC", "nips_2022_DTD9BRDWtkn", "2zPFigHuL_v", "AA-0lmAlSY9", "Uoa6C9u-XaI", "aA3pvkp3dmL", "nips_2022_DTD9BRDWtkn", "nips_2022_DTD9BRDWtkn", "nips_2022_DTD9BRDWtkn", "nips_2022_DTD9BRDWtkn" ]
nips_2022_UwzrP-B38jK	Learning Robust Rule Representations for Abstract Reasoning via Internal Inferences	Abstract reasoning, as one of the hallmarks of human intelligence, involves collecting information, identifying abstract rules, and applying the rules to solve new problems. Although neural networks have achieved human-level performances in several tasks, the abstract reasoning techniques still far lag behind due to th...	Accept	I thank the authors for their submission and active participation in the discussions. The paper presents a method for rule representation learning that can be transferred accross tasks. All reviewers unanimously agree that this paper's strengths outweigh its weaknesses. In particular, reviewers found the method to be w...	test	[ "sqNW9tB8nZP", "K6JRYNO3Ufa", "zJLHmA8Li7H", "0wRG-ss8IV_", "MXqeButhyJN", "SGr1fD18KUd", "EWsMJ9Z25AC", "BHHSEshkXlD", "Fjxk4XF5Yp3", "pDPJxw5633d", "NOBjU2ooHIe", "OqtB3WfHHAo", "UNs35GXuSuq", "MTBUAjzQmZJ", "RTYPNrJAed", "nlgju2MKWd", "m6PLFniDhEh", "5_7g21t617c", "2ZHUe_4Zczi...	[ "author", "author", "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "official_re...	[ " Thanks again for your insightful reviews. We are grateful to see that our response addresses your concerns, and appreciate your positive feedback. \n\nWe are glad to provide additional responses if you have any further questions.", " Thanks for your insightful and positive feedback.\n\nSince relations are much ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 6, 7, 8, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 5, 5, 5 ]	[ "zJLHmA8Li7H", "SGr1fD18KUd", "Fjxk4XF5Yp3", "MXqeButhyJN", "EWsMJ9Z25AC", "NOBjU2ooHIe", "BHHSEshkXlD", "pDPJxw5633d", "2ZHUe_4Zczi", "5_7g21t617c", "m6PLFniDhEh", "UNs35GXuSuq", "nlgju2MKWd", "RTYPNrJAed", "nips_2022_UwzrP-B38jK", "nips_2022_UwzrP-B38jK", "nips_2022_UwzrP-B38jK", ...
nips_2022_8N1NDRGQSQ	CalFAT: Calibrated Federated Adversarial Training with Label Skewness	Recent studies have shown that, like traditional machine learning, federated learning (FL) is also vulnerable to adversarial attacks. To improve the adversarial robustness of FL, federated adversarial training (FAT) methods have been proposed to apply adversarial training locally before global aggregation. Although the...	Accept	This submission aims to ensure adversarial robustness in federated learning when label skewness exists among different local agents. The main idea of the proposed solution is to calibrate the logits to balance the predicted marginal label probabilities. Most of the reivewers found the topic studied in this work relevan...	test	[ "f6HVuX9kha", "plby1aZTqjb", "kiRImXYHlIs", "96Ze1xoFfo", "QjZkfP2JfnC", "wZw4gN6WuFg", "bkevMw1ygTT", "Rlziwi0m_EC", "fN26kx5K2W0", "gjQ1wHuufWG", "_W1PL_kr5j7", "AdNLAT3aiTT", "n71A4y5mrO", "uRaNWE01BPU", "28fbP8GRvF6", "c4OK30GjTr2", "n-z2bWUE19I" ]	[ "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I thank the authors for further clarification and additional experiments, which solved most of my concerns. I would like to improve my score.", " We would like to gently remind the reviewer of any follow-up clarifications or questions that we can do our best to address in the remaining limited time. We hope our...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 8, 3, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4 ]	[ "n-z2bWUE19I", "n-z2bWUE19I", "c4OK30GjTr2", "QjZkfP2JfnC", "28fbP8GRvF6", "c4OK30GjTr2", "n-z2bWUE19I", "nips_2022_8N1NDRGQSQ", "n-z2bWUE19I", "n-z2bWUE19I", "c4OK30GjTr2", "c4OK30GjTr2", "c4OK30GjTr2", "28fbP8GRvF6", "nips_2022_8N1NDRGQSQ", "nips_2022_8N1NDRGQSQ", "nips_2022_8N1NDR...
nips_2022_02YXg0OZdG	Eliciting Thinking Hierarchy without a Prior	When we use the wisdom of the crowds, we usually rank the answers according to their popularity, especially when we cannot verify the answers. However, this can be very dangerous when the majority make systematic mistakes. A fundamental question arises: can we build a hierarchy among the answers without any prior where...	Accept	This work proposes the framework to elicit people's "thinking hierarchy" that helps improve the wisdom of the crowd even if the majority is wrong. The reviewers overall appreciate the main idea of the work and believe it makes a nice contribution to the literature. There have been some questions/concerns raised about ...	train	[ "-5lhHqjCO6", "StCCchwU2JB", "a2V2g6WJoK4", "EYwFGcYPErc", "h5jqDva0-2O", "wOhodG-OBa", "XPWZX3ai2J", "9I30YdnJ7_O", "0y7DF4ymDUt", "YEGu13yzJBx", "mQjlDje-Ip1", "KcUdT6ehdSj", "HPh0leCp0oD", "gID0Vxif1FL" ]	[ "official_reviewer", "author", "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I suggest the authors put the running time analysis in the main body of the paper, probably even the dynamic programming-based algorithm.\n\nI think the exponential running time in the number of possible answers is not very practical and limits the application of the model proposed in the paper with the algorithm...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 7, 5, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 1, 3, 4 ]	[ "StCCchwU2JB", "a2V2g6WJoK4", "EYwFGcYPErc", "9I30YdnJ7_O", "wOhodG-OBa", "XPWZX3ai2J", "gID0Vxif1FL", "HPh0leCp0oD", "KcUdT6ehdSj", "mQjlDje-Ip1", "nips_2022_02YXg0OZdG", "nips_2022_02YXg0OZdG", "nips_2022_02YXg0OZdG", "nips_2022_02YXg0OZdG" ]
nips_2022_vphSm8QmLFm	GBA: A Tuning-free Approach to Switch between Synchronous and Asynchronous Training for Recommendation Models	High-concurrency asynchronous training upon parameter server (PS) architecture and high-performance synchronous training upon all-reduce (AR) architecture are the most commonly deployed distributed training modes for recommendation models. Although synchronous AR training is designed to have higher training efficiency,...	Accept	The paper identifies and illustrates a practically relevant challenge for training of deep learning-based recommender systems on distributed architectures: switching between synchronous and asynchronous training modes. The proposed mechanism called global batch gradient aggregation (GBA) is simple but mitigates the nee...	train	[ "7nxfwetkVWE", "dysZe_zxyMT", "cFQbpBkWe4n", "AP738OwxNqE", "NZ-mj_M92v", "Zd4JDuyzfWO", "j4kLHL7su2p", "PW-EByxcA13", "6xJHTFesvss" ]	[ "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We have done many experiments and studies to understand this phenomenon in terms of different hyper-parameters. As we said in our last response, it is a common phenomenon encountered by our researchers / developers, which remains an open question in the community. And a comprehensive theoretical analysis of t...	[ -1, -1, -1, -1, -1, -1, 5, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, 4, 4, 3 ]	[ "dysZe_zxyMT", "AP738OwxNqE", "PW-EByxcA13", "j4kLHL7su2p", "nips_2022_vphSm8QmLFm", "6xJHTFesvss", "nips_2022_vphSm8QmLFm", "nips_2022_vphSm8QmLFm", "nips_2022_vphSm8QmLFm" ]
nips_2022_L0OKHqYe_FU	Online Neural Sequence Detection with Hierarchical Dirichlet Point Process	Neural sequence detection plays a vital role in neuroscience research. Recent impressive works utilize convolutive nonnegative matrix factorization and Neyman-Scott process to solve this problem. However, they still face two limitations. Firstly, they accommodate the entire dataset into memory and perform iterative upd...	Accept	This paper describes a hierarchical Bayesian latent model to identify neural sequences from spike data. Especially in neuroscience, detection of patterns in neural sequences is an important computational problem as the infrared patterns are useful for characterizing brain activity. The key problem is reminiscent of clu...	val	[ "bcmb-I61QEI", "qyHRt_TiFaG", "6a5QAsIWdG3", "iaSRF5mrOa", "8Lz7h8tzIrD", "h2LJx6pJ9OI", "XuYRv3yyAT", "DkYrlQYnxz", "lrBaq9V7FIP", "4W95EG0R52", "fEu130Htzv_", "SV2HmJ1T74", "fm-gIlBZ8TQ", "NfAQtdf4O8" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you very much for considering our responses and for revising the score! We will be happy to address any further questions or concerns about the work.", " I thank the authors for conducting additional experiments and explanation. I think the paper has overall improved as a result of authors efforts to addr...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 6, 3, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 3, 4 ]	[ "qyHRt_TiFaG", "XuYRv3yyAT", "iaSRF5mrOa", "DkYrlQYnxz", "h2LJx6pJ9OI", "4W95EG0R52", "fEu130Htzv_", "SV2HmJ1T74", "fm-gIlBZ8TQ", "NfAQtdf4O8", "nips_2022_L0OKHqYe_FU", "nips_2022_L0OKHqYe_FU", "nips_2022_L0OKHqYe_FU", "nips_2022_L0OKHqYe_FU" ]
nips_2022_i0FnLiIRj6U	Iterative Scene Graph Generation	The task of scene graph generation entails identifying object entities and their corresponding interaction predicates in a given image (or video). Due to the combinatorially large solution space, existing approaches to scene graph generation assume certain factorization of the joint distribution to make the estimation ...	Accept	The authors propose a new approach for end-to-end training of predicting scene graphs from images (different from the traditional two-stage approach.) The key observation that the fixed factorization approach can be suboptimal due to error compounding is reasonable and is supported by the experiment results. The propos...	train	[ "6PM6tMEeVQ3", "3WOzgdhgl_", "7NniQZoTMHz", "Z3u49Ju2iD", "Gzl4gPop8rS", "Zhh3HvpfK-S", "5-7_XAscQ3n", "4t-bMd3Hlan", "GX2JqQy0los", "cixcvBtLei", "ITGhrDfGp27", "MhCB75CxxnW" ]	[ "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for reading our rebuttal.\n\nOverall it is difficult to objectively argue about novelty. However, we would like to highlight that, to the best of our knowledge, the high-level idea of iterative scene graph generation is novel and has not appeared elsewhere. The unique benefit of such formulation is its ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 6, 4 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 3, 4 ]	[ "7NniQZoTMHz", "7NniQZoTMHz", "5-7_XAscQ3n", "Gzl4gPop8rS", "Zhh3HvpfK-S", "ITGhrDfGp27", "MhCB75CxxnW", "GX2JqQy0los", "cixcvBtLei", "nips_2022_i0FnLiIRj6U", "nips_2022_i0FnLiIRj6U", "nips_2022_i0FnLiIRj6U" ]
nips_2022_4btNeXKFAQ	Low-rank Optimal Transport: Approximation, Statistics and Debiasing	The matching principles behind optimal transport (OT) play an increasingly important role in machine learning, a trend which can be observed when OT is used to disambiguate datasets in applications (e.g. single-cell genomics) or used to improve more complex methods (e.g. balanced attention in transformers or self-super...	Accept	Overall: The paper focuses on advancing our knowledge, understanding and practical ability to leverage low-rank factorizations in optimal transport. Reviews: The paper received four reviews. 4 accepts (all confident). It seems that there are several reviewers that will champion the paper for publication. The reviewers...	train	[ "D63rmfOOMT", "L6raeKsMQSw", "ZzjlhR8TM4g", "CoRsCEOvwXY", "sNTMWKWaeqI", "_madUWpFsik", "1lX4wL7BZVf", "Q4QfSYcgBwU", "bylzDOqdX8P", "-dmg1DeKZYv", "CSGcaIa5UAQ", "5NeA860eDt", "Jxho_pci88", "NNhoScgngon", "iy1NtmBpI-" ]	[ "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewer:\n\nMany thanks for your appreciation and supporting comments. Thank you very much for your suggestions. What follows is a simple and fairly open-ended response on the items you have raised:\n\nOn item (1): this is indeed an important subject. One might hope to start from the simplest possible cases...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 7, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 3 ]	[ "ZzjlhR8TM4g", "CoRsCEOvwXY", "_madUWpFsik", "-dmg1DeKZYv", "iy1NtmBpI-", "1lX4wL7BZVf", "NNhoScgngon", "bylzDOqdX8P", "Jxho_pci88", "5NeA860eDt", "nips_2022_4btNeXKFAQ", "nips_2022_4btNeXKFAQ", "nips_2022_4btNeXKFAQ", "nips_2022_4btNeXKFAQ", "nips_2022_4btNeXKFAQ" ]
nips_2022_ZCGDqdK0zG	Fast Distance Oracles for Any Symmetric Norm	In the \emph{Distance Oracle} problem, the goal is to preprocess $n$ vectors $x_1, x_2, \cdots, x_n$ in a $d$-dimensional normed space $(\mathbb{X}^d, \\| \cdot \\|_l)$ into a cheap data structure, so that given a query vector $q \in \mathbb{X}^d$, all distances $\\| q - x_i \\|_l$ to the data points $\{x_i\}_{i\in [n]}$ c...	Accept	Reviewers found the problem, the results and the techniques (very) interesting. The main concerns were about the practicality of the results (esp. lack of experiments) and presentation (notably various typos). The presentation issues appear to be easily fixable with a careful pass over the paper. Ultimately, the posit...	train	[ "GtBozmUobaK", "lOX45ej4g4Z", "8eCxxWO9jtL", "s2MZT6UvM6N", "JXbQAzPsz9i", "JDjim616G8e" ]	[ "official_reviewer", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " The authors try to explain why their contributions are meaningful and I can appreciate their results better now. I won't change my score due to the presentation issues I mentioned. I noticed that the other reviewers gave it a score of 2 regarding presentation, so overall this makes for a hard paper to read and th...	[ -1, -1, 4, 7, 6, 6 ]	[ -1, -1, 3, 4, 3, 4 ]	[ "lOX45ej4g4Z", "nips_2022_ZCGDqdK0zG", "nips_2022_ZCGDqdK0zG", "nips_2022_ZCGDqdK0zG", "nips_2022_ZCGDqdK0zG", "nips_2022_ZCGDqdK0zG" ]
nips_2022_5Fg3XoHjQ4r	Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning	In this paper, we target image-based person-to-person virtual try-on in the presence of diverse poses and large viewpoint variations. Existing methods are restricted in this setting as they estimate garment warping flows mainly based on 2D poses and appearance, which omits the geometric prior of the 3D human body shape...	Accept	This paper received 4 positive reviews: 2xBA + WA+ A. All reviewers acknowledged that the proposed approach is simple and effective, it is well presented, and the claims are supported by strong empirical performance and extensive evaluation on several datasets. The remaining questions and concerns were addressed in the...	train	[ "1-9KJCdlcpH", "QeblDBVF9p2", "pBRiFtjMmXh", "w5NtzzSwH2e", "ToWEVmBAYK8", "0Z40M1rNi2c", "KFIpJn4fwyJ", "KuCUwYRRjN", "6P_6lNUw5tYb", "lx3z3Ba-hox", "yJWzMNQ8rFb", "nh6sd9Bn5qH", "YtBLrnUN1rV", "QXhiYjxyfm_", "81jZfgvdPYV" ]	[ "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Authors addressed some of my concerns, especially w.r.t. the issues in the experimental setup. I am increasing my rating to borderline accept.", " Thanks authors for the clarifications. I will update my rating after cross-checking prior art on datasets and metrics.", " Dear Reviewer bUCs,\nWe have tried to ad...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 5, 6, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 4, 4, 3 ]	[ "QeblDBVF9p2", "pBRiFtjMmXh", "81jZfgvdPYV", "KuCUwYRRjN", "81jZfgvdPYV", "81jZfgvdPYV", "YtBLrnUN1rV", "YtBLrnUN1rV", "QXhiYjxyfm_", "nh6sd9Bn5qH", "nips_2022_5Fg3XoHjQ4r", "nips_2022_5Fg3XoHjQ4r", "nips_2022_5Fg3XoHjQ4r", "nips_2022_5Fg3XoHjQ4r", "nips_2022_5Fg3XoHjQ4r" ]
nips_2022_A6AFK_JwrIW	Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs	Despite recent success in using the invariance principle for out-of-distribution (OOD) generalization on Euclidean data (e.g., images), studies on graph data are still limited. Different from images, the complex nature of graphs poses unique challenges to adopting the invariance principle. In particular, distribution s...	Accept	Graph NNs have proven to work considerably well in the in-distribution setting. However, they fail when test data come from a different distribution than test, as shown by previous work. This paper aligns with recent works, and aims to study how to obtain invariance to shifts described by the assumed causal model. Th...	train	[ "r2RIXtKNbla", "S1bqdlssm5", "sljHPndSz10", "jEpHzrG_t6Q", "4QxPTlCckHq", "m5cqnTem94U", "TKdSok44XPx", "vcBTqL_hQCE", "d0RtVHMK78F", "FXxheM6AMS", "dFr_5epjaPU", "2FKaOONi37x", "DmiNtKjTt5y", "LYbuh2ZkEeW", "jn1spJuh4d", "F8ZSKXbHpev", "-L3EP8eMBwDr", "KiTJrAO0O7y", "naQjdPVFgi"...	[ "author", "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author"...	[ " Thank you again for your time and efforts in reviewing our paper, and for your valuable comments that helped us to strengthen the paper!", " I have raised my rating from 5 to 6.", " Dear Reviewer QGqs,\n\nAs the window for discussion is closing, we’d be grateful if you can confirm whether our follow-up respon...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 2, 4 ]	[ "S1bqdlssm5", "DmiNtKjTt5y", "d0RtVHMK78F", "4QxPTlCckHq", "2A0iG8sKLK0", "_3N7KSxXut", "d0RtVHMK78F", "d0RtVHMK78F", "Z2qV6MFcwex", "dFr_5epjaPU", "2FKaOONi37x", "nLDNj69gWII", "HpFhRSN-bHX", "_3N7KSxXut", "nLDNj69gWII", "HpFhRSN-bHX", "_3N7KSxXut", "nips_2022_A6AFK_JwrIW", "HpF...
nips_2022_8ow4YReXH9j	Ultra-marginal Feature Importance	Scientists frequently prioritize learning from data rather than training the best possible model; however, research in machine learning often prioritizes the latter. Marginal contribution feature importance (MCI) was developed to break this trend by providing a useful framework for quantifying the relationships in data...	Reject	This work makes a significant contribution to establishing the theoretical foundations for feature importance. The authors suggest a set of axioms that a feature importance score should have and introduce an algorithm that computes a feature importance score that has these required properties. In addition to the theore...	test	[ "3ot3m0hNLrn", "ewha3HILv8", "ntW74zE5Qe", "XK_W3Rlwho5", "VtIYzDrjPs", "WaZ9SL-ykTD", "e38xCaIre9F", "d7R91_ghF9j", "9RQz7cHKntc", "_GnTpbI5Aw8", "NwuyOR4Mu_y", "9wN6OclgVuZ", "GWYg-6tCCK6", "DVgoiRwG8kW", "2V13JwMbB7", "rNHhmDsyDQ", "U4Rf1jgFoRA", "_qWyE-gOGk", "BGO8ffog3jG", ...	[ "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_r...	[ " Sounds good, I think this resolves all my concerns. And yes I think discussing these points and making the changes from the previous posts will improve the paper. Best of luck.", " Reply #6 and #13. \n\nAh, we see what you are saying now. Thank you for catching this. We will ensure that this wording is chan...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 6, 7, 8 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 4, 4 ]	[ "ewha3HILv8", "ntW74zE5Qe", "XK_W3Rlwho5", "VtIYzDrjPs", "_GnTpbI5Aw8", "d7R91_ghF9j", "9RQz7cHKntc", "nips_2022_8ow4YReXH9j", "GWYg-6tCCK6", "NwuyOR4Mu_y", "DVgoiRwG8kW", "BGO8ffog3jG", "OoWGp8aDyUP", "qQqno9Q_V28", "qQqno9Q_V28", "ffXFHK96AyV", "nips_2022_8ow4YReXH9j", "nips_2022...
nips_2022_OcNoF7qA4t	Non-Linear Coordination Graphs	Value decomposition multi-agent reinforcement learning methods learn the global value function as a mixing of each agent's individual utility functions. Coordination graphs (CGs) represent a higher-order decomposition by incorporating pairwise payoff functions and thus is supposed to have a more powerful representation...	Accept	This paper is a very clear accept. The reviews had only minor quibbles, which I trust the authors will address in their final version.	train	[ "21tERp8EpxK", "wmCSWCNUu9", "SvcOYpYq0ec", "b1G5FVsTqK5", "6sAii9JjuZH", "ZvPvEEuQm_P", "5jPzI5gwRpW", "ltWhrJF0i8c", "xZ8avovdPOp", "HENoABMXRy", "O1atqOkoHr4_", "w36tsjttoP6", "WmjhtPuMGPtG", "uvcjTJ87OWd", "B9Ta4WYArJ", "L7-ipKKD1SWx", "qG4ndEzbKgG", "01OSMHkARYl", "FF0UzYZx5...	[ "author", "author", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_r...	[ " Thanks for the reviewer's work！", " Thanks for the reviewer's work!", " Thanks for the reviewer's work.", " Thanks for the reviewer's work!", " I appreciate your explanations, the point is clearer now. I would like to thanks the authors, I do not have any further concern or doubt.", " Dear authors,\n\nT...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 7, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 5, 4, 5 ]	[ "6sAii9JjuZH", "HENoABMXRy", "xZ8avovdPOp", "ZvPvEEuQm_P", "5jPzI5gwRpW", "01OSMHkARYl", "O1atqOkoHr4_", "HENoABMXRy", "B9Ta4WYArJ", "WmjhtPuMGPtG", "w36tsjttoP6", "KO6QiPjGG_", "axYDqevqpc5", "FF0UzYZx5H", "FF0UzYZx5H", "01OSMHkARYl", "01OSMHkARYl", "nips_2022_OcNoF7qA4t", "nips...
nips_2022_0xbhGxgzd1t	ComGAN: Unsupervised Disentanglement and Segmentation via Image Composition	We propose ComGAN, a simple unsupervised generative model, which simultaneously generates realistic images and high semantic masks under an adversarial loss and a binary regularization. In this paper, we first investigate two kinds of trivial solutions in the compositional generation process, and demonstrate their sour...	Accept	The paper proposes a compositional GAN model with a novel network architecture that solves the vanishing gradient problem underlying trivial solutions. The proposed model achieves strong results on image disentanglement and unsupervised segmentation tasks. The rebuttals by the authors have successfully addressed most...	train	[ "Q5FzOXK9bxU", "ckyEektziO8", "ftycyuBW4FE", "7-sKLOpgu4S", "xtsai1wwrkL", "1EizKaEfZRmX", "Jf2S7GrKlw", "THgGlMRDfV9", "7XiG-cjyLN", "_FjnonuxRS", "HNcjZEda-a", "2rT2J8hnJ9C", "gOR6FOs7E-s", "O0FJYU9yOI8", "MT0a2UoUuy9" ]	[ "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for your response and appreciation. We are glad that our feedback addressed your concerns. We will be encouraged if you would like to raise your rating accordingly. Please let us know if there is anything else we can do to make the paper better.", " Thank you for your comments. My main concerns regard...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 3 ]	[ "ftycyuBW4FE", "7XiG-cjyLN", "THgGlMRDfV9", "HNcjZEda-a", "MT0a2UoUuy9", "O0FJYU9yOI8", "gOR6FOs7E-s", "MT0a2UoUuy9", "_FjnonuxRS", "gOR6FOs7E-s", "2rT2J8hnJ9C", "O0FJYU9yOI8", "nips_2022_0xbhGxgzd1t", "nips_2022_0xbhGxgzd1t", "nips_2022_0xbhGxgzd1t" ]
nips_2022_9TsP2Gg0CM	Homomorphic Matrix Completion	In recommendation systems, global positioning, system identification and mobile social networks, it is a fundamental routine that a server completes a low-rank matrix from an observed subset of its entries. However, sending data to a cloud server raises up the data privacy concern due to eavesdropping attacks and the s...	Accept	This paper concerns privacy-preserving matrix completion in a distributed manner. The communication security is based on homomorphic encryption, while the notion of privacy is defined as the subspace-aware join differential privacy. The paper received a mixed evaluation from the reviewers, ranging from accept (7) to ...	train	[ "qeuIRXvvywy", "YCPArOklwc8", "sbcS4S0de8G", "RXAdDqTY7VY", "L1woWzBytdQ", "lF5WNN1THmu", "71-6MD5EA1", "GXvsEnkc-A", "DjkNb73cJ4", "IRj2ANJxif", "yjvzL6L9Tv", "b7y5nQsj1_" ]	[ "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " The reviewer (Reviewer UWAB) raised an interesting question about the “block distributed” setting, which is a special case covered by the proposed homomorphic encryption framework, and the provided theoretical results naturally apply.\n\nAbout parameter k and the true rank r of data matrix M, the authors restat...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 3, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 2, 4, 3 ]	[ "lF5WNN1THmu", "RXAdDqTY7VY", "nips_2022_9TsP2Gg0CM", "L1woWzBytdQ", "b7y5nQsj1_", "71-6MD5EA1", "yjvzL6L9Tv", "DjkNb73cJ4", "IRj2ANJxif", "nips_2022_9TsP2Gg0CM", "nips_2022_9TsP2Gg0CM", "nips_2022_9TsP2Gg0CM" ]
nips_2022_hqtSdpAK39W	Cluster Randomized Designs for One-Sided Bipartite Experiments	The conclusions of randomized controlled trials may be biased when the outcome of one unit depends on the treatment status of other units, a problem known as \textit{interference}. In this work, we study interference in the setting of one-sided bipartite experiments in which the experimental units---where treatments ar...	Accept	This well-written paper proposes a possibly-new experiment-design problem where there is interference. This interference is modeled by a bipartite graph where one side has the "experimental" units and the other has "interference" units. The purpose of the interference units is to facilitate interactions between the exp...	train	[ "lpflBoVxhEi", "Ds8456iKEn", "Bqgya1mITDe", "oGrinrWPGBG", "Mtt4B5m5_W3", "Cv5RlMzJArJ", "gVibI99Gia2", "TGeWQVdjtW1", "XWmONu0QnN", "5B91TnILfca", "5PG7XLEYZwu", "w1jd2o4Nw0a", "2K3lZFbswDy", "YJqrSamKLIL", "BwJHhGdpo9C", "RmV-sbJ-x-P" ]	[ "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We thank the reviewer for their comments. It would be great if you can comment on our response addressing the reviewer's major concerns. Thanks.", " Thank you for the response. We will include the power-law graph experiments and the discussion of the choice of balanced design in our revision.\n\nThank you also ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 6, 6, 8 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 2, 4, 4, 3 ]	[ "TGeWQVdjtW1", "Bqgya1mITDe", "Cv5RlMzJArJ", "RmV-sbJ-x-P", "BwJHhGdpo9C", "BwJHhGdpo9C", "YJqrSamKLIL", "2K3lZFbswDy", "w1jd2o4Nw0a", "w1jd2o4Nw0a", "w1jd2o4Nw0a", "nips_2022_hqtSdpAK39W", "nips_2022_hqtSdpAK39W", "nips_2022_hqtSdpAK39W", "nips_2022_hqtSdpAK39W", "nips_2022_hqtSdpAK39...
nips_2022_Qy1D9JyMBg0	Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts	Large sparsely-activated models have obtained excellent performance in multiple domains. However, such models are typically trained on a single modality at a time. We present the Language-Image MoE, LIMoE, a sparse mixture of experts model capable of multimodal learning. LIMoE accepts both images and text simultaneousl...	Accept	The authors use a mixture-of-experts model in a multimodal setting. the reviewers consider the work technically strong and interesting; the AC concurs.	val	[ "B6ZCZYC1T7", "PmbzIETIF1b", "RvOiFmC6rI", "ef5EsWLCWx2", "JRS04qAEjY2", "ArHoIMwFjFG", "sIMwbn_zz63", "TyKsUg5SFtl" ]	[ "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I have read the reviews and responses from all reviewers and personally thank the authors for the clarifications and answers to my individual questions. I find them satisfactory and increase 1 point to support this paper for that reason. ", " Many thanks for the time spent reviewing and your feedback, and for n...	[ -1, -1, -1, -1, -1, 8, 6, 7 ]	[ -1, -1, -1, -1, -1, 4, 3, 5 ]	[ "RvOiFmC6rI", "TyKsUg5SFtl", "ef5EsWLCWx2", "sIMwbn_zz63", "ArHoIMwFjFG", "nips_2022_Qy1D9JyMBg0", "nips_2022_Qy1D9JyMBg0", "nips_2022_Qy1D9JyMBg0" ]
nips_2022_Rym8_qTIB7o	Node-oriented Spectral Filtering for Graph Neural Networks	Graph neural networks (GNNs) have shown remarkable performance on homophilic graph data while being far less impressive when handling non-homophilic graph data due to the inherent low-pass filtering property of GNNs. In general, since the real-world graphs are often a complex mixture of diverse subgraph patterns, learn...	Reject	The paper has mixed reviews. While some reviewers feel that the paper is novel and interesting, other reviewers think that additional experiments are needed to justify the proposed method and that the proposed methods are somewhat incremental. The paper will benefit from another revision that will address the raised co...	train	[ "FS9foMR8Wz", "QXTP63OZxC", "ks5-7wuJ8B", "ixB1MKd8-GR", "_L8QqRMfafo", "LZJoSAwS9xI", "rHtGfij-K4g", "_rZL1bqc-BE", "LXaEolx23ON", "EiKcx175i_F", "OT1d5ifU-i", "ccbc0uLqqW", "UzrZf0pywO4", "HqPnXEnOh3", "vq7ip3M49W" ]	[ "author", "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " \n We sincerely thank the reviewers for their comments and regret not having completely eliminated your concerns about the ability of our NFGNN model in handling heterogeneous graphs. In the new comment, the reviewer thinks that, for a challenging complex graph mixing of homophily and heterophily, the new method...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 7, 7, 4 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 3, 3 ]	[ "QXTP63OZxC", "ks5-7wuJ8B", "_L8QqRMfafo", "HqPnXEnOh3", "rHtGfij-K4g", "vq7ip3M49W", "vq7ip3M49W", "HqPnXEnOh3", "UzrZf0pywO4", "ccbc0uLqqW", "ccbc0uLqqW", "nips_2022_Rym8_qTIB7o", "nips_2022_Rym8_qTIB7o", "nips_2022_Rym8_qTIB7o", "nips_2022_Rym8_qTIB7o" ]
nips_2022_vaxPmiHE3S	EGRU: Event-based GRU for activity-sparse inference and learning	The scalability of recurrent neural networks (RNNs) is hindered by the sequential dependence of each time step's computation on the previous time step's output. Therefore, one way to speed up and scale RNNs is to reduce the computation required at each time step independent of model size and task. In this paper, we pro...	Reject	This paper introduces an event-based GRU to obtain an efficient continuous-time RNN. Although the method is sound and can work on a series of small sequence modeling tasks, there are multiple issues with the significance of the results of the paper which I point out in the following: 1) There have been significant adv...	train	[ "2qR1aNY8z5C", "m7IegRZyBOO", "Qkb8-2reMPX", "Ny3-JOMj2gA", "2WoExm0S3l", "4mcsrXG5v7P", "YdPqvrVoZkz", "GB1jLG9W1WU", "4MS-onvnKnQ" ]	[ "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I have raised my score and the authors have done good work in the rebuttal.", " We would like to thank all their reviewers for their constructive comments and questions. Please note that we have uploaded an updated version of our main text as well as supplement. We have indicated all major changes using blue co...	[ -1, -1, -1, -1, -1, -1, 6, 6, 5 ]	[ -1, -1, -1, -1, -1, -1, 4, 2, 4 ]	[ "2WoExm0S3l", "nips_2022_vaxPmiHE3S", "4MS-onvnKnQ", "GB1jLG9W1WU", "4mcsrXG5v7P", "YdPqvrVoZkz", "nips_2022_vaxPmiHE3S", "nips_2022_vaxPmiHE3S", "nips_2022_vaxPmiHE3S" ]
nips_2022_mkEPog9HiV	Structure-Preserving 3D Garment Modeling with Neural Sewing Machines	3D Garment modeling is a critical and challenging topic in the area of computer vision and graphics, with increasing attention focused on garment representation learning, garment reconstruction, and controllable garment manipulation. Whereas existing methods were constrained to model garments under specific categories ...	Accept	This paper was reviewed by four experts in the field. Based on the reviewers' feedback, the decision is to recommend the paper for acceptance to NeurIPS 2022. The reviewers did raise some valuable concerns that should be addressed in the final camera-ready version of the paper. For example, 1) the evaluation on real-w...	train	[ "2jQovDItzOC", "ysq4Uf1ZsQC", "VWN7tqlNT7m", "q7_JWf_J18J", "jO4MgvZOeU", "LUKbgSFm0j", "9KPvE188mEt", "oELCji1O1n", "SC4ePiMzEMv", "h--w5bBkMuU", "s32zVx52JP7", "z2YxgGZpZT8", "Ku8DI5Frk6", "DmpNUG2NkR" ]	[ "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " The authors have addressed my concerns thoroughly. After reading other reviews, I decided to change my score to 6. If accepted, please consider adding the details above into the appendix so that the paper is self-contained.", " The authors have tried to address most of my concerns and I appreciate the hard work...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 6, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 5, 4, 4 ]	[ "oELCji1O1n", "z2YxgGZpZT8", "DmpNUG2NkR", "Ku8DI5Frk6", "z2YxgGZpZT8", "z2YxgGZpZT8", "DmpNUG2NkR", "Ku8DI5Frk6", "z2YxgGZpZT8", "s32zVx52JP7", "nips_2022_mkEPog9HiV", "nips_2022_mkEPog9HiV", "nips_2022_mkEPog9HiV", "nips_2022_mkEPog9HiV" ]
nips_2022_sQiEJLPt1Qh	Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions	A deep neural network using rectified linear units represents a continuous piecewise linear (CPWL) function and vice versa. Recent results in the literature estimated that the number of neurons needed to exactly represent any CPWL function grows exponentially with the number of pieces or exponentially in terms of the f...	Accept	Three reviewers agree that this work meets the bar for acceptance, rating it weak accept, weak accept, and accept. The work provides bounds for approximating continuous piecewise linear functions by ReLU networks and an algorithm. Reviewers praised the novelty and significance, and were positive about clarifications of...	val	[ "FuTgJ7JeAVL", "EmqG-Eodao", "t_4bcZQ1Udg", "omm9eSnT0_", "hrTaPpmcJnf", "6AUFJIbQ_m2", "3bocoUo6va8", "TcqHa7IFev", "t3PzKX8OvJY", "ts616n8krl", "ZcatGefNO0x", "Ae1S7CQgPdW", "f_wDlbG3OC7", "4bMck8ZCeIT" ]	[ "author", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for letting us know! Again, we thank the reviewer for taking the time and effort to review the paper and provide thoughtful comments.", " If you see it fit, please consider giving the paper a higher rating to ensure acceptance. Thank you!", " The authors have addressed all my concerns and I also app...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 2, 3, 4 ]	[ "t_4bcZQ1Udg", "hrTaPpmcJnf", "ts616n8krl", "hrTaPpmcJnf", "ZcatGefNO0x", "4bMck8ZCeIT", "f_wDlbG3OC7", "Ae1S7CQgPdW", "4bMck8ZCeIT", "f_wDlbG3OC7", "Ae1S7CQgPdW", "nips_2022_sQiEJLPt1Qh", "nips_2022_sQiEJLPt1Qh", "nips_2022_sQiEJLPt1Qh" ]
nips_2022_hTxYJAKY85	Learning Graph-embedded Key-event Back-tracing for Object Tracking in Event Clouds	Event data-based object tracking is attracting attention increasingly. Unfortunately, the unusual data structure caused by the unique sensing mechanism poses great challenges in designing downstream algorithms. To tackle such challenges, existing methods usually re-organize raw event data (or event clouds) with the ev...	Accept	The paper receives overall positive reviews and rebuttal has resolved the reviewer's concerns. The paper proposes a new framework that directly takes raw event clouds as inputs for object tracking. Reviewers agree that this innovation is inspiring. AC agrees and recommends accepting the paper.	train	[ "ZTzl3iiP2IM", "141dAUsLyD", "ff4rgapF5Wt", "8m5L1iAkaer", "bzpXEMFL8aB", "bLsXVZ7bRf", "MOQWgaR8kjG", "8IMpOjhuT7O", "Y94VUVsi-Bn", "tGVOPVxnuFP", "Ly-sNealNJ", "1iYFgRJ8ADE", "xNVqYUty0g", "pRkDff6m_1", "TI5VfXtChsZ", "puXdzygeIhK", "Rd863boTA9", "3-P75O81wmb", "I4ycaaUPqfN", ...	[ "author", "author", "author", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewer kchd\n\nThanks for your time and efforts in reviewing our submission 3911, as well as the recognition of our work. We think we have answered your questions clearly and directly. We are also glad to answer them if you have any more questions. Thanks.\n\nThe authors", " Dear *Reviewer Vqd3...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 5, 5, 4 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 4 ]	[ "YNkQdJ4Y9Is", "XZnEKfIhyIP", "I4ycaaUPqfN", "bzpXEMFL8aB", "bLsXVZ7bRf", "MOQWgaR8kjG", "Rd863boTA9", "nips_2022_hTxYJAKY85", "YNkQdJ4Y9Is", "YNkQdJ4Y9Is", "XZnEKfIhyIP", "XZnEKfIhyIP", "XZnEKfIhyIP", "I4ycaaUPqfN", "I4ycaaUPqfN", "3-P75O81wmb", "3-P75O81wmb", "nips_2022_hTxYJAKY8...
nips_2022_JpxsSAecqq	OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression	This paper presents a language-powered paradigm for ordinal regression. Existing methods usually treat each rank as a category and employ a set of weights to learn these concepts. These methods are easy to overfit and usually attain unsatisfactory performance as the learned concepts are mainly derived from the training...	Accept	The paper proposes a language-powered model for ordinal regression tasks, based on CLIP. Language prototypes are constructed from sentences with rank categories via the CLIP paper encoder, and then optimizing the CLIP model by language prototype and image feature matching. To further boost the ordinality, this paper in...	train	[ "CInkYtdhlTU", "Sk7N0LpmQNG", "R6T-oxvBoH", "y7rOjysufUg", "lg9mLdJNRZ", "DfG-v5xK-02", "WrvWzrrSj27", "BSxZLrYS5yX", "7eK6HUt4TmJ", "LyRY83YjaE3", "nCKqkKU7M-w", "qGm3KgJzWgY", "lH2ESQDEaI8", "G6tcqzkAfh6" ]	[ "author", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewer RX3e,\n\nThanks again for your valuable advice and supportive comments! We have responded to your initial comments. We are looking forward to your feedback and will be happy to answer any further questions you may have.", " Dear Reviewer PfAX,\n\nThanks again for your valuable advice and supportiv...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 4 ]	[ "G6tcqzkAfh6", "lH2ESQDEaI8", "y7rOjysufUg", "lg9mLdJNRZ", "qGm3KgJzWgY", "qGm3KgJzWgY", "qGm3KgJzWgY", "qGm3KgJzWgY", "G6tcqzkAfh6", "G6tcqzkAfh6", "lH2ESQDEaI8", "nips_2022_JpxsSAecqq", "nips_2022_JpxsSAecqq", "nips_2022_JpxsSAecqq" ]
nips_2022_c7sI8S-YIS_	Unsupervised learning of features and object boundaries from local prediction	A visual system has to learn both which features to extract from images and how to group locations into (proto-)objects. Those two aspects are usually dealt with separately, although predictability is discussed as a cue for both. To incorporate features and boundaries into the same model, we model a layer of feature ma...	Reject	There was some disagreement on the value of this work. The paper received 1 strong accept, 1 accept and 1 reject. The positive reviewers recommended the paper to be accepted because it proposes a novel unsupervised approach to semantic segmentation and contours detection and because of connections to neuroscience. Some...	train	[ "6tVsqHnNz_C", "_Ffma96J2C", "ds73YZwr42d", "_6yMEaMQXAS", "BsEWyeyawX", "jczdLNkZp-C", "1ly81u-Z5U", "qsG84cF9yTi", "R6M35nvMgte", "7XDycZlHogd", "XJPkU7xsNZ2", "tb63_P9OTlL", "x2E-Q2XCQlc", "dE2aQadmvJ" ]	[ "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you! We are glad that we could clarify things and hope to see this paper accepted, too, of course.\n\nAlso, If our submission is accepted, we will be allowed an additional content page for the camera-ready version. Thus, we will most certainly have space to add a paragraph on texture vs. contour grouping.",...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 7, 8 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 4 ]	[ "_Ffma96J2C", "_6yMEaMQXAS", "R6M35nvMgte", "BsEWyeyawX", "jczdLNkZp-C", "dE2aQadmvJ", "dE2aQadmvJ", "x2E-Q2XCQlc", "x2E-Q2XCQlc", "tb63_P9OTlL", "nips_2022_c7sI8S-YIS_", "nips_2022_c7sI8S-YIS_", "nips_2022_c7sI8S-YIS_", "nips_2022_c7sI8S-YIS_" ]
nips_2022_2dxsDFaESK	Amortized Projection Optimization for Sliced Wasserstein Generative Models	Seeking informative projecting directions has been an important task in utilizing sliced Wasserstein distance in applications. However, finding these directions usually requires an iterative optimization procedure over the space of projecting directions, which is computationally expensive. Moreover, the computational i...	Accept	During the author-reviewer discussions, the authors have addressed most of the concerns raised by the reviewers, leading to original scores being raised. During the reviewer discussions, the disagreement among reviewers about the demonstration of computational benefits was discussed. At this point, the merits of the pa...	train	[ "U6-y_e6ztzC", "dn4tUqgzYOB", "Nmn9jKpRtf", "3kFABmEVUE", "O1wG5C7FaZz", "BDisx06EQoQY", "N4GMkZVkoBR", "1wH3JElO2-X", "6uaI_p3qP10", "ZZlqA_HR0NZ", "aVawyKWwJZN", "5k8BKoH0RIN", "u0XY8zBl-pm", "HOdqtxHKrWc", "oYxj3bWpmHC", "sZ187ct_GZ_", "fcOM4NJ9mQY", "GGLMUIQ9N96", "ynS7fFW-l1...	[ "author", "author", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewer hTkP,\n\nWe have addressed your concerns in our responses. Given that the discussion deadline is only a few hours from now and you are the only one that gives a negative score on our paper, we would like to hear your feedback. Please feel free to raise questions if you have other concerns.\n\nBest r...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 8, 6, 4 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 4, 5 ]	[ "cspeNTOajs", "fcOM4NJ9mQY", "3kFABmEVUE", "N4GMkZVkoBR", "oYxj3bWpmHC", "1wH3JElO2-X", "GGLMUIQ9N96", "u0XY8zBl-pm", "nips_2022_2dxsDFaESK", "cspeNTOajs", "cspeNTOajs", "cspeNTOajs", "ynS7fFW-l1s", "GGLMUIQ9N96", "fcOM4NJ9mQY", "fcOM4NJ9mQY", "nips_2022_2dxsDFaESK", "nips_2022_2dx...
nips_2022_O4Q39aQFz0Y	Revisiting Sliced Wasserstein on Images: From Vectorization to Convolution	The conventional sliced Wasserstein is defined between two probability measures that have realizations as \textit{vectors}. When comparing two probability measures over images, practitioners first need to vectorize images and then project them to one-dimensional space by using matrix multiplication between the sample m...	Accept	The paper presents a new slicing methods for the Wasserstein distance between probability measures over images based on convolution operators. This way memory requirements can be reduced and locality can be better preserved. Experiments are conducted on generative modeling problems. Reviewers noted that the idea of con...	test	[ "M4J8IJ-1Vxe", "emLl_afR8N3", "8JQjK40-YY0", "YJNnI1PF33J", "tgAbAGLdxN", "E2biCTnkNhm", "Tpc0jojym58", "0RDgSuVH0f6", "BLUJJs_eIr", "IKa2WLX5Zf", "QnHMW9YcBiF", "qGFDo9XS-5", "rev8ApFa_bx", "PHcVTqX0f3B", "AEVbxvl_-tR", "36RtXFup99I", "IbjkUWK8HCU", "TOr2f2X6pa0", "4iXAPbjJZ65",...	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_...	[ " Dear Reviewer GwEg,\n\nGiven that we already addressed your concerns, the discussion deadline is only a few hours from now, and you give a negative score on the paper, we would like to hear your feedback on whether our response is sufficient to change your opinion about the paper. Please feel free to raise quest...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 2, 4, 4 ]	[ "at4FObFbYmS", "pyB9oNb056G", "YJNnI1PF33J", "E2biCTnkNhm", "Tpc0jojym58", "Tpc0jojym58", "0RDgSuVH0f6", "DKLz2q7Txwt", "nips_2022_O4Q39aQFz0Y", "AEVbxvl_-tR", "qGFDo9XS-5", "AEVbxvl_-tR", "nips_2022_O4Q39aQFz0Y", "36RtXFup99I", "36RtXFup99I", "4iXAPbjJZ65", "OpFvwfaPC3", "w1F8zItX...
nips_2022_r-6Z1SJbCpv	Towards Learning Universal Hyperparameter Optimizers with Transformers	Meta-learning hyperparameter optimization (HPO) algorithms from prior experiments is a promising approach to improve optimization efficiency over objective functions from a similar distribution. However, existing methods are restricted to learning from experiments sharing the same set of hyperparameters. In this paper,...	Accept	In this work, authors investigate whether Transformers can be used for hyperparameter optimization. The work is interesting and authors outline how they frame the problem and solve practical difficulties. The resulting method is shown to be able to learn to HPO from historical HPO runs and text-based metadata. Some imp...	train	[ "arGI5wuwzdv", "hBFl6_-vJ6", "vuj4zRulnLkz", "mllX7rCmqTT", "7NXnu3TTJI1", "ey56R17-vCN", "wdSze6tiSim", "1L3PCyqqjv9", "c-9IWZ1700p", "AqzpBG-8MR", "wQVTpqnC1Rr", "YvpCk0M4lId", "sB1LLeGtRb" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewer,\n\nOnce again, thank you very much for your valuable time spent on our submission and your thoughtful reviews!\nAs the Author-Reviewer discussion period is coming to an end soon, we wanted to check in with you if we have addressed your questions and concerns, and if we provided all information requ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 7, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 4, 5 ]	[ "1L3PCyqqjv9", "vuj4zRulnLkz", "mllX7rCmqTT", "7NXnu3TTJI1", "sB1LLeGtRb", "YvpCk0M4lId", "wQVTpqnC1Rr", "AqzpBG-8MR", "nips_2022_r-6Z1SJbCpv", "nips_2022_r-6Z1SJbCpv", "nips_2022_r-6Z1SJbCpv", "nips_2022_r-6Z1SJbCpv", "nips_2022_r-6Z1SJbCpv" ]
nips_2022_H3o9a6l0wz	Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition	Identity-invariant facial expression recognition (FER) has been one of the challenging computer vision tasks. Since conventional FER schemes do not explicitly address the inter-identity variation of facial expressions, their neural network models still operate depending on facial identity. This paper proposes to quanti...	Accept	Authors propose a new strategy for a hard problem that reviewers found compelling and novel. The experimental details are complex and we encourage the authors to address the many issues the reviewers raise.	train	[ "rwZNEzH8b-L", "Rd25pMj1E3e", "lqE7BRkdnSC", "dbYGO683V9g", "7ArDRopBFUE", "lHckhRYyew", "9UfQWDxQ_v", "MbAXqAfQpnS", "A2itZp8qOaM", "6XSC0PiI8Jv", "8Cb8xqZjxXG", "jt1H1KhpCkl" ]	[ "author", "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Many thanks for the good comments from the reviewer. Although feature normalization is not applied in the inference phase, we can expect positive performance. The reason is as follows:\n- The mechanism of allowing the model to learn domain-invariant features through training domains (i.e., training IDs in our cas...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 5, 4 ]	[ "dbYGO683V9g", "lqE7BRkdnSC", "9UfQWDxQ_v", "A2itZp8qOaM", "lHckhRYyew", "MbAXqAfQpnS", "8Cb8xqZjxXG", "jt1H1KhpCkl", "6XSC0PiI8Jv", "nips_2022_H3o9a6l0wz", "nips_2022_H3o9a6l0wz", "nips_2022_H3o9a6l0wz" ]
nips_2022_xdZs1kf-va	I2Q: A Fully Decentralized Q-Learning Algorithm	Fully decentralized multi-agent reinforcement learning has shown great potentials for many real-world cooperative tasks, where the global information, \textit{e.g.}, the actions of other agents, is not accessible. Although independent Q-learning is widely used for decentralized training, the transition probabilities ar...	Accept	The paper presents a novel method for dealing with nonstationarity in decentralized multi-agent reinforcement learning (MARL). While there are some concerns about the level of novelty, the approach is interesting and presented well. There are also concerns about the discussion and comparison with the state-of-the-art i...	train	[ "yIBJZ3pRWer", "aAaGEOVeuLZ", "q6ZLhxQBjH", "k9F9eRE5G2L", "8lb_AwMVDv", "4Y-HNlGbBVq", "1K2t9p-ytC", "Mrh9EHqMDu3", "jmJUJkgbSxX", "TsixHy2Vct9", "zdaFcnUy6Fc" ]	[ "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I thank the authors for their detailed rebuttal. While the authors responded to all my points, they remain points of weakness in the paper. Particularly as learning a stochastic model is hard and it is unclear how learning a stochastic model would perform in practice in this case. Also, I still find the theoretic...	[ -1, -1, -1, -1, -1, -1, -1, -1, 8, 5, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 4 ]	[ "8lb_AwMVDv", "1K2t9p-ytC", "zdaFcnUy6Fc", "zdaFcnUy6Fc", "zdaFcnUy6Fc", "TsixHy2Vct9", "jmJUJkgbSxX", "nips_2022_xdZs1kf-va", "nips_2022_xdZs1kf-va", "nips_2022_xdZs1kf-va", "nips_2022_xdZs1kf-va" ]
nips_2022_awdyRVnfQKX	HierSpeech: Bridging the Gap between Text and Speech by Hierarchical Variational Inference using Self-supervised Representations for Speech Synthesis	This paper presents HierSpeech, a high-quality end-to-end text-to-speech (TTS) system based on a hierarchical conditional variational autoencoder (VAE) utilizing self-supervised speech representations. Recently, single-stage TTS systems, which directly generate raw speech waveform from text, have been getting interest ...	Accept	all reviewers agree * the paper is interesting and novel * the proposed method has solid experiments and good results * paper is well written this paper should be accepted to the conference.	train	[ "pJ_VBqH82j", "TTapwyv7Jw8", "Z4mYN8VBs_6", "4kMMs09D2vd", "NeCDaJDx83n", "NIFbXTtAwKz", "qbD_Sqw1y8h" ]	[ "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I have read the authors' responses which addressed my questions. I will keep the original rating.", " We appreciate for your helpful comments and suggestions. We have provided responses to your questions below to address your concerns.\n\n>Q1. Adding PP to the VITS posterior encoder is helpful. The posterior en...	[ -1, -1, -1, -1, 6, 6, 7 ]	[ -1, -1, -1, -1, 3, 3, 4 ]	[ "TTapwyv7Jw8", "qbD_Sqw1y8h", "NIFbXTtAwKz", "NeCDaJDx83n", "nips_2022_awdyRVnfQKX", "nips_2022_awdyRVnfQKX", "nips_2022_awdyRVnfQKX" ]
nips_2022_XA4ru9mfxTP	Unifying Voxel-based Representation with Transformer for 3D Object Detection	In this work, we present a unified framework for multi-modality 3D object detection, named UVTR. The proposed method aims to unify multi-modality representations in the voxel space for accurate and robust single- or cross-modality 3D detection. To this end, the modality-specific space is first designed to represent dif...	Accept	The paper proposes a multimodal system for 3d object detection and 3 expert reviewers vote for its acceptance, after rebuttal, based on their appreciation of the good improvements brought by multimodality, and due various interesting details of the system. I agree with reviewer Bb8v that the writing should be polished...	train	[ "EBOzXUvZtw7", "QtD_CnMjGLD", "sDIp2eYg7a", "hA1nrdY3rbl", "4i8199g5Oi", "Ie7f6IIvjg2", "5TbNmbWhlD", "q-mbK1IORWd", "3XfmiG9QTh", "Mu0NI3SoEQI", "14UrmhefQzI", "M0fBxfEC_lE", "yiQBcypy7wO", "T_1IOSu7Qy3", "HbpSkyoYtaQ", "PapyNMDTWHx" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewer Bb8v,\n\nWe follow your suggestions and keep polishing the paper. Because the revision cannot be uploaded now, we attach the revision of Section 3.2 below. Hope it can address your remaining concern.\n\n3.2 Cross-modality Interaction\n\nWith the unified representation in space $\\mathbf{V}_I$ an...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 4 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4 ]	[ "hA1nrdY3rbl", "14UrmhefQzI", "hA1nrdY3rbl", "5TbNmbWhlD", "Ie7f6IIvjg2", "HbpSkyoYtaQ", "PapyNMDTWHx", "PapyNMDTWHx", "HbpSkyoYtaQ", "HbpSkyoYtaQ", "T_1IOSu7Qy3", "nips_2022_XA4ru9mfxTP", "nips_2022_XA4ru9mfxTP", "nips_2022_XA4ru9mfxTP", "nips_2022_XA4ru9mfxTP", "nips_2022_XA4ru9mfxTP...
nips_2022_FzdmrTUyZ4g	Monte Carlo Tree Descent for Black-Box Optimization	The key to Black-Box Optimization is to efficiently search through input regions with potentially widely-varying numerical properties, to achieve low-regret descent and fast progress toward the optima. Monte Carlo Tree Search (MCTS) methods have recently been introduced to improve Bayesian optimization by computing bet...	Accept	This paper proposes a novel combination of Bayesian optimization and Monte Carlo Tree Search for more sample-efficient black-box optimization. The method is adding complexity, but the empirical results are thorough and show a clear benefit. The reviewers on this paper did not come to unanimous decision, but the clear ...	train	[ "yIflUXxssCW", "ZczyHKspmTU", "PkZP1TxeQq", "ol-8hjV4MX8", "KcKoOpHhgK2", "BuCCRbEp9c", "bEjGmK4LDlp", "t4f9SIZLbXR", "laAYALK9Hiw" ]	[ "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " After reading the responses by the authors, several issues have been addressed.\nHowever, I still think the motivation of this paper is not convincing enough for me. \nDue to the sufficient experimental results, I'm increasing my score to weak accept. ", " Thank you for your feedback. We address the specific qu...	[ -1, -1, -1, -1, -1, 6, 7, 2, 6 ]	[ -1, -1, -1, -1, -1, 4, 2, 5, 4 ]	[ "ZczyHKspmTU", "laAYALK9Hiw", "t4f9SIZLbXR", "bEjGmK4LDlp", "BuCCRbEp9c", "nips_2022_FzdmrTUyZ4g", "nips_2022_FzdmrTUyZ4g", "nips_2022_FzdmrTUyZ4g", "nips_2022_FzdmrTUyZ4g" ]
nips_2022_E3LgJdPEkP	A Mean-Field Game Approach to Cloud Resource Management with Function Approximation	Reinforcement learning (RL) has gained increasing popularity for resource management in cloud services such as serverless computing. As self-interested users compete for shared resources in a cluster, the multi-tenancy nature of serverless platforms necessitates multi-agent reinforcement learning (MARL) solutions, whic...	Accept	I agree with the reviewers that this is a well-written paper on an interesting application of mean-field games. The paper is a nice blend of theoretical developments and experimental evaluations. I believe that it will be well-received by the NeurIPS community and recommend acceptance.	test	[ "aTsMvAVdXqo", "AKd5hb8bPg4", "Srpze0q2Fmm", "n5ThODp-KED", "uP1olfpnIbL", "tl512tHeJCH", "RXmjQBk6v9f", "nrJ8aC7-Hv", "yu1jcSF6t6s", "w0DRwt-4Kp", "rAoAVpOCKT_", "dgUbSxAFuKG", "9aQuAoHcu9R", "4cqXLWViweC", "wrxrVmFBlL" ]	[ "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks to the authors for their detailed reply. I expected the paper to discuss cloud resource management in more detail since the title is \"A Mean-Field Game Approach to Cloud Resource Management with ***\". However, putting a lot of scenario descriptions in the appendix does not help the reader to understand h...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 3, 2 ]	[ "RXmjQBk6v9f", "Srpze0q2Fmm", "n5ThODp-KED", "uP1olfpnIbL", "tl512tHeJCH", "wrxrVmFBlL", "nrJ8aC7-Hv", "9aQuAoHcu9R", "w0DRwt-4Kp", "dgUbSxAFuKG", "4cqXLWViweC", "nips_2022_E3LgJdPEkP", "nips_2022_E3LgJdPEkP", "nips_2022_E3LgJdPEkP", "nips_2022_E3LgJdPEkP" ]
nips_2022__vfyuJaXFug	Translation-equivariant Representation in Recurrent Networks with a Continuous Manifold of Attractors	Equivariant representation is necessary for the brain and artificial perceptual systems to faithfully represent the stimulus under some (Lie) group transformations. However, it remains unknown how recurrent neural circuits in the brain represent the stimulus equivariantly, nor the neural representation of abstract grou...	Accept	The paper constructs recurrent neural circuits that represent stimuli equivariantly with respect to a given symmetry, taking the example of the 1D translation group. Most Reviewers were positively impressed by the general framing of the problem in terms of group theory and the elucidation of a connection between Contin...	train	[ "Ls4JqrnHKlB", "akCK7uZpfe", "spPuxrMYP2", "yPoM_UHVRt4", "LTHpjhS3SZ7", "EYWPN5QzOB0", "LideWf1GSS0L", "q3tERdXMRaL", "Do5YKCcthOK", "pSqGTJ4eHjR", "-hBUcuS8RZH", "B_XkWhXjuzF", "BhssC8rjE_", "wpRyVOiyDSi" ]	[ "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for your responses. I appreciate the additional discussions that the authors have promised. It is clear to me that this submission is an important contribution.", " Thanks for the reviewer's suggestion. We will definitely comment on the first paper in our revised manuscript.\nAlso, the temporal scalin...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 8, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 2 ]	[ "EYWPN5QzOB0", "spPuxrMYP2", "LTHpjhS3SZ7", "pSqGTJ4eHjR", "BhssC8rjE_", "B_XkWhXjuzF", "-hBUcuS8RZH", "-hBUcuS8RZH", "wpRyVOiyDSi", "wpRyVOiyDSi", "nips_2022__vfyuJaXFug", "nips_2022__vfyuJaXFug", "nips_2022__vfyuJaXFug", "nips_2022__vfyuJaXFug" ]
nips_2022_RO0wSr3R7y-	3DILG: Irregular Latent Grids for 3D Generative Modeling	We propose a new representation for encoding 3D shapes as neural fields. The representation is designed to be compatible with the transformer architecture and to benefit both shape reconstruction and shape generation. Existing works on neural fields are grid-based representations with latents being defined on a regular...	Accept	All reviewers agree to accept this work, which presents a creative new shape representation for 3D generative modeling. The negative aspects raised by the reviewers are fairly minor, and most were addressed during the rebuttal phase (please be sure to incorporate all comments/additional results into the final camera-r...	train	[ "77169KRl2XV", "8cmN-0rM6bN", "fqF8YzxvzX0", "7Uw9QhaY9Ay", "kCgZYCvsK5P", "0bvJKyXZcD", "kB8PK959tdT8", "EXOojKbopJ5", "QXa1WljqFPg", "YzLqcqNaKn2", "xMJOIOzK9uU", "rY6lB1VEEQe", "HMyyucKlkJk", "4Tqf7Ah7MRr" ]	[ "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewers,\n\nThanks again for the review. Since the author/reviewer discussion phase is ending tomorrow, we would like to ask if our comments helped clarify your concerns or if there are additional questions we can help with.", " We thank the reviewers for their comments. We are happy to see that reviewer...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 7, 5, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 4, 4 ]	[ "nips_2022_RO0wSr3R7y-", "nips_2022_RO0wSr3R7y-", "7Uw9QhaY9Ay", "4Tqf7Ah7MRr", "0bvJKyXZcD", "HMyyucKlkJk", "EXOojKbopJ5", "rY6lB1VEEQe", "YzLqcqNaKn2", "xMJOIOzK9uU", "nips_2022_RO0wSr3R7y-", "nips_2022_RO0wSr3R7y-", "nips_2022_RO0wSr3R7y-", "nips_2022_RO0wSr3R7y-" ]
nips_2022_N0tKCpMhA2	Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$-Means Clustering	Vertical federated learning (VFL), where data features are stored in multiple parties distributively, is an important area in machine learning. However, the communication complexity for VFL is typically very high. In this paper, we propose a unified framework by constructing \emph{coresets} in a distributed fashion for...	Accept	The reviewers have converged around the idea that the paper proposes an interesting approach to vertical federated learning; they also conclude that the authors have provided replies to reviews that answered questions and provided useful clarifications, which encourages the acceptance of the paper. I will stress the ...	train	[ "mr7ZzPRVDv", "q08fGS38zS", "biHk5a9hUv2", "UyOeA-P_A2k", "R6Tx3FCcP_t", "-o33SwYDY8", "aft5knh6wgd", "vTRGBXodyS0", "peLxfWfXQAi", "RjJkufbFxlR" ]	[ "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks a lot for your support! We will address your comments in the updated version.", " Thanks a lot for your support!", " I have read the author's rebuttal and I keep my score. \n\n", " Thanks for the response, which addresses my concerns and questions. I will maintain my scores. \n\nBTW, it would be bett...	[ -1, -1, -1, -1, -1, -1, -1, 7, 6, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, 3, 4, 4 ]	[ "UyOeA-P_A2k", "biHk5a9hUv2", "R6Tx3FCcP_t", "aft5knh6wgd", "RjJkufbFxlR", "peLxfWfXQAi", "vTRGBXodyS0", "nips_2022_N0tKCpMhA2", "nips_2022_N0tKCpMhA2", "nips_2022_N0tKCpMhA2" ]
nips_2022_5pvB6IH_9UZ	CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis	A persistent challenge in conditional image synthesis has been to generate diverse output images from the same input image despite only one output image being observed per input image. GAN-based methods are prone to mode collapse, which leads to low diversity. To get around this, we leverage Implicit Maximum Likelihood...	Accept	This paper introduces a conditional image synthesis method based on Implicit Maximum Likelihood Estimation (IMLE). Compared to previous work CIMLE, the paper has introduced a divide-and-conquer method to accurately estimate latent code without evaluating many samples. The paper has received consistently positive review...	train	[ "AXC-myKlLiT", "rmRMyYCXHw", "YZuYGmxH4B-", "15jk5Et9mY", "WpYb705hDdW", "CYs5k9tfwTs", "NlhJehHcrVZ", "P-hRK30QSiV", "jeny33SzRsa", "v9PQrIvts9KW", "9zDuvpVFTwr", "0gUioh31eDT", "CFbRucya0p", "baCBlUW6kv", "3YnjSVpya-", "YRp69Ep-Qn-", "ND3Oh4C5ssH" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " For Q1: We want to remind the reviewer that while the generation procedure is hierarchical, the number of such levels of hierarchy is a lot smaller than the number of parallel generated samples, so it does not compromise generation speed. On the contrary, the hierarchical generation procedure greatly improves...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 5, 5, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 3, 4 ]	[ "rmRMyYCXHw", "v9PQrIvts9KW", "WpYb705hDdW", "NlhJehHcrVZ", "jeny33SzRsa", "v9PQrIvts9KW", "9zDuvpVFTwr", "0gUioh31eDT", "ND3Oh4C5ssH", "YRp69Ep-Qn-", "3YnjSVpya-", "baCBlUW6kv", "nips_2022_5pvB6IH_9UZ", "nips_2022_5pvB6IH_9UZ", "nips_2022_5pvB6IH_9UZ", "nips_2022_5pvB6IH_9UZ", "nips...
nips_2022_xK6wRfL2mv7	Sharpness-Aware Training for Free	Modern deep neural networks (DNNs) have achieved state-of-the-art performances but are typically over-parameterized. The over-parameterization may result in undesirably large generalization error in the absence of other customized training strategies. Recently, a line of research under the name of Sharpness-Aware Minim...	Accept	This paper proposes a novel optimization method, called SAF, for reaching flat minima. The main claim is that the proposed method does not suffer from computational overhead of SAM-like methods which is typically 2x SGD. The proposed method is based on a novel loss that minimizes the KL-divergence between the output of...	train	[ "2zAxlUm3Kti", "L3jgQMXH15g", "vIKGV4z6l5x", "mEX6EDV6Cx", "TkvVkaa4Yqw", "ty2xiB6ifDX", "HJMF1c05cW", "m87V0qVB34h", "ZSyF0kYHZL", "YB8WeDS1UhL", "SQoPLdPvB86", "TJnObd90-Lt", "s8jyIpPVK5j", "t0AEd_w-uRz", "RJaATRurbu0", "5xZbbcF1Ams", "3qx2-s-ERgq", "ahZOHFzJn98", "EBEODdjDYMU"...	[ "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", ...	[ " Thanks for the response and the additional experiments.", " Continued of A11 \n\nFor the gap between the reported results of [2,37] and [21], the reported results of ViT-S/32-SAM are precisely listed as\n\n\|Vit-S/32-SAM Reported in [2] \| Vit-S/32-SAM Reported in [37] \|Vit-S/32-SAM Reported in [21] \|Vit-S/32-SAM...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 3, 6, 4 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 2 ]	[ "5xZbbcF1Ams", "vIKGV4z6l5x", "HJMF1c05cW", "TkvVkaa4Yqw", "ty2xiB6ifDX", "TJnObd90-Lt", "TJnObd90-Lt", "ZSyF0kYHZL", "SQoPLdPvB86", "RJaATRurbu0", "tXCVWY5tWaH", "s8jyIpPVK5j", "t0AEd_w-uRz", "ahZOHFzJn98", "EBEODdjDYMU", "3qx2-s-ERgq", "nips_2022_xK6wRfL2mv7", "nips_2022_xK6wRfL2...
nips_2022_m8YYs8nJF3T	Distributional Convergence of the Sliced Wasserstein Process	Motivated by the statistical and computational challenges of computing Wasserstein distances in high-dimensional contexts, machine learning researchers have defined modified Wasserstein distances based on computing distances between one-dimensional projections of the measures. Different choices of how to aggregate thes...	Accept	After the rebuttal period, the reviewers have come to an agreement on the paper being novel, interesting, the contributions being significant. The rebuttal also addressed most of the concerns, though I agree with reviewer 84Jv on the comment that experiments on non-compact settings would be a plus to see the limits of ...	train	[ "cwgP2zx2Vii", "k9xW54zb86z", "o4YFl0mqXrkK", "F6xLuGl4f0", "4mlQmbiPOug", "19M2B1exfom", "JiMF7Fl-YVz", "3GlabsjbJwT", "9xtqoZl2AD1", "RXqFTdE_Yl", "C9TBUJ-yeW4" ]	[ "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you very much for your detailed response and the changes made to the document (especially the clarification for the example with $p=1$). Also, I agree with you on the particular issue raised in 2, thanks for pointing out my misunderstanding. \n\nAfter reading the other reviews and responses, the authors' go...	[ -1, -1, -1, -1, -1, -1, -1, 7, 6, 6, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, 3, 4, 4, 4 ]	[ "4mlQmbiPOug", "F6xLuGl4f0", "JiMF7Fl-YVz", "C9TBUJ-yeW4", "RXqFTdE_Yl", "9xtqoZl2AD1", "3GlabsjbJwT", "nips_2022_m8YYs8nJF3T", "nips_2022_m8YYs8nJF3T", "nips_2022_m8YYs8nJF3T", "nips_2022_m8YYs8nJF3T" ]
nips_2022_Iksst2czYoB	Stochastic Multiple Target Sampling Gradient Descent	Sampling from an unnormalized target distribution is an essential problem with many applications in probabilistic inference. Stein Variational Gradient Descent (SVGD) has been shown to be a powerful method that iteratively updates a set of particles to approximate the distribution of interest. Furthermore, when analysi...	Accept	The paper presents a particle-based method to approximate multiple target distributions simultaneously. The proposed particle-updating dynamics is shown to decrease the KL to every target (makes a Pareto improvement), and the resulting particles prefer the intersection of all targets (Pareto common) which differs it fr...	train	[ "cupTJmaE6q4", "04yPUOg10rQ", "PsAWGzbNwdt", "poWZ6ak3Ou", "Cyqoskf1jqP", "i5XNcvNIhB4", "koGlyUM6G-X", "1Bfufgou0Nx", "0lgSxt6XD0l", "y8Gdyrr4TB9", "XhH5KTWEHfj", "h8q2OUzC4GI", "x2IxHQwXoKQ", "1Z-7GKS_AIC", "ViKY-WVuwGb", "yn0fQ1UXHHh", "ZpUQy03IVbA" ]	[ "author", "author", "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We would like to thank the reviewer for spending their time evaluating our paper and providing detailed feedbacks. We will include the intuition behind our objective function in the next version of our manuscript, as you suggested.\n\nBest,\n\nAuthors.\n", " Thank you for recognizing our efforts and providing c...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 2 ]	[ "i5XNcvNIhB4", "PsAWGzbNwdt", "0lgSxt6XD0l", "Cyqoskf1jqP", "koGlyUM6G-X", "1Bfufgou0Nx", "y8Gdyrr4TB9", "x2IxHQwXoKQ", "1Z-7GKS_AIC", "XhH5KTWEHfj", "h8q2OUzC4GI", "ZpUQy03IVbA", "yn0fQ1UXHHh", "ViKY-WVuwGb", "nips_2022_Iksst2czYoB", "nips_2022_Iksst2czYoB", "nips_2022_Iksst2czYoB" ...
nips_2022_wjSHd5nDeo	Multi-Sample Training for Neural Image Compression	This paper considers the problem of lossy neural image compression (NIC). Current state-of-the-art (SOTA) methods adopt uniform posterior to approximate quantization noise, and single-sample pathwise estimator to approximate the gradient of evidence lower bound (ELBO). In this paper, we propose to train NIC with multip...	Accept	This paper studies the problem of neural image compression (NIC). Standard methods for NIC use a "single-sample pathwise estimator" to estimate the ELBO gradients to optimize the rate-distortion loss function. This paper improves the estimation by using multiple samples, leading to better compression results. Experimen...	train	[ "56y96darhdk", "PQoHuGSmBi", "Ny2RouvxdTy", "v4aJmL50Ttl", "8CNsGglSXvi", "lgSh_2GBplB", "HRQ06AI7Qtq", "TleKjGrsrq", "cRRrwb8PzY", "1GM-KYtKbMi", "J9yMpqSeTu", "4Uj0Cz5AjFGV", "LgKnwGh4iYFM", "3snQs7Rmrlj", "rZtzMELI-_7", "LacQHX8kFGh", "Zi7t_8odtLC" ]	[ "official_reviewer", "author", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I thank the authors for the detailed response, which has addressed most of my concerns and provided interesting new insights to the problem. \n\nI think the paper made a good contribution that connects and fills in the blank of existing literature, and I've raised my score. ", " Hi, thanks for your feedback.\n\...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 2 ]	[ "rZtzMELI-_7", "Ny2RouvxdTy", "Zi7t_8odtLC", "HRQ06AI7Qtq", "nips_2022_wjSHd5nDeo", "nips_2022_wjSHd5nDeo", "LacQHX8kFGh", "LacQHX8kFGh", "Zi7t_8odtLC", "Zi7t_8odtLC", "Zi7t_8odtLC", "rZtzMELI-_7", "rZtzMELI-_7", "rZtzMELI-_7", "nips_2022_wjSHd5nDeo", "nips_2022_wjSHd5nDeo", "nips_20...
nips_2022_duBoAyn9aI	Controllable and Lossless Non-Autoregressive End-to-End Text-to-Speech	Some recent studies have demonstrated the feasibility of single-stage neural text-to-speech, which does not need to generate mel-spectrograms but generates the raw waveforms directly from the text. Single-stage text-to-speech often faces two problems: a) the one-to-many mapping problem due to multiple speech variations...	Reject	I am in agreement with the last 2 reviewers. 1) there are many concerns about the technical correctness of the paper that can be improved 2) more thorough evaluations and experiments are needed i'm marking this as reject and i encourage the authors to address reviewer comments and resubmit.	train	[ "WV0tv4qeW4", "yFt4cs2CFWh", "iBiKeYD3qeZ", "DIxy90qzpB7", "Gw5EC0UvIW", "9MapiaTetKZ", "p0QN4RQprK-", "NdwOQdChi4F", "GNQWvVoFPsK", "jOS9v0Q5Pyk", "956tD4Lppgm", "IA7XbuUFB_H", "bfpfXvp0_aq" ]	[ "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewer xNqf:\n\nWe thank you for the precious review time and valuable comments. We have provided corresponding responses and results, which we believe have covered your concerns. We hope to further discuss with you whether or not your concerns have been addressed. Please let us know if you still have any ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 8, 3, 3 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 5, 4 ]	[ "bfpfXvp0_aq", "IA7XbuUFB_H", "bfpfXvp0_aq", "bfpfXvp0_aq", "IA7XbuUFB_H", "IA7XbuUFB_H", "IA7XbuUFB_H", "IA7XbuUFB_H", "956tD4Lppgm", "956tD4Lppgm", "nips_2022_duBoAyn9aI", "nips_2022_duBoAyn9aI", "nips_2022_duBoAyn9aI" ]
nips_2022_pZsAwqUgnAs	Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions	Stochastic gradient descent (SGD) is a pillar of modern machine learning, serving as the go-to optimization algorithm for a diverse array of problems. While the empirical success of SGD is often attributed to its computational efficiency and favorable generalization behavior, neither effect is well understood and disen...	Accept	The paper addresses an important question regarding the trajectories of SGD in high-dimensional settings. The theoretical derivations of the paper builds on top of prior works but nonetheless is sound. Most reviewers agree that the paper advances the knowledge in this area.	train	[ "0MS6RX4G3PJ", "vrNdYZgPcI6", "p-pZC3fgnvB", "0hM9xrKDuo", "GoH2AGk-S5t", "m99_GzrFNgV", "VZFxqPhjXHn", "DT95QKDYHj7Q", "t6iXHfSOZXL8", "26dzc896CsR9", "XKDJvTnLpq", "8aeIVgnkdpI", "zzUb5uZUKMf", "Qz48jbosjRh", "jnzDMhrNDdj", "UEkYuZs1qfg" ]	[ "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We agree with the reviewer that it is very interesting to examine the evolution of the ICR during training. We have added an experiment that shows the ICR is roughly constant over training [see Supplementary Materials zip file], yielding at least some preliminary empirical support for the practical utility of...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 5, 6, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 2, 4, 4, 4 ]	[ "p-pZC3fgnvB", "8aeIVgnkdpI", "GoH2AGk-S5t", "m99_GzrFNgV", "UEkYuZs1qfg", "VZFxqPhjXHn", "jnzDMhrNDdj", "t6iXHfSOZXL8", "Qz48jbosjRh", "XKDJvTnLpq", "zzUb5uZUKMf", "nips_2022_pZsAwqUgnAs", "nips_2022_pZsAwqUgnAs", "nips_2022_pZsAwqUgnAs", "nips_2022_pZsAwqUgnAs", "nips_2022_pZsAwqUgnA...
nips_2022_OtxyysUdBE	FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction	Most cross-device federated learning (FL) studies focus on the model-homogeneous setting where the global server model and local client models are identical. However, such constraint not only excludes low-end clients who would otherwise make unique contributions to model training but also restrains clients from trainin...	Accept	To better handle the case that each client is with heterogeneous device resources, this paper presents a model-heterogeneous federated learning algorithm FedRolex. FedRolex rolls the submodel in each federated iteration, in order to train the parameters of the global model on the global data distribution. Experimental ...	train	[ "8ntybHCno2Q", "w5tyCt5O-MZ", "FLVZlP3uMAY", "49l0mlTPQED", "7_K38_z_eer", "dnz8l6B8f_t", "w4MBf6NWmlI", "gSpOtYKurW5", "1jlXHIUVjro", "DbY_u0LWAzl", "E9XjD4CZ-Xe", "J2O0MWJUlHP", "MI1_Ziwe0gk", "gHtDiePuuys", "azZoU7_Lz70", "NYwVcjRkepO", "8uLTmCciM5g", "fjMYyofwZC", "bDnZPef0fJ...	[ "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "officia...	[ " Dear Reviewer 5GU1,\n\nWe have included the rebuttal results and discussion into the revision, and have uploaded the latest version. Thanks again for the time and valuable comments. We really appreciate it.", " Thank the authors for your response, which addressed my major concerns. I have improved my ratting.",...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 8, 5, 8, 4 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 5, 4 ]	[ "FLVZlP3uMAY", "8YlqFSRnPnX", "E9XjD4CZ-Xe", "MH5WBbl2PEc", "lHzfU7_OEtN", "8YlqFSRnPnX", "CznujbdzLMH", "lHzfU7_OEtN", "lHzfU7_OEtN", "CznujbdzLMH", "CznujbdzLMH", "MH5WBbl2PEc", "MH5WBbl2PEc", "MH5WBbl2PEc", "MH5WBbl2PEc", "MH5WBbl2PEc", "MH5WBbl2PEc", "8YlqFSRnPnX", "8YlqFSRnP...
nips_2022_ZVuzllOOHS	Differentially Private Covariance Revisited	In this paper, we present two new algorithms for covariance estimation under concentrated differential privacy (zCDP). The first algorithm achieves a Frobenius error of $\tilde{O}(d^{1/4}\sqrt{\mathrm{tr}}/\sqrt{n} + \sqrt{d}/n)$, where $\mathrm{tr}$ is the trace of the covariance matrix. By taking $\mathrm{tr}=1$, t...	Accept	The reviewers all concurred that the main result of this paper is quite interesting. It privately estimates the covariance better than established methods in particular parameter regimes. Given the clear accept sentiments towards this paper, there was little additional discussion.	train	[ "Eo1OYFaFH4", "t-WwuMZbvD", "4BtpOocJBxg", "lMKnkDLSJfX", "y14RQa8c6Fu", "Xk_LWa8zMbS", "2ThMVST8Wq3", "abDYpy77f1M", "NE3Ccu3NM_", "fe0mhJOY9JZ", "HR9NEXNKvIu", "mC6-yuQ0c7-" ]	[ "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for your helpful comments!\n\nQuestion 2: This is an interesting observation! Yes, we can use simhash to transform a vector from the unit sphere to one in $\\\\{-1/\\sqrt{d},1/\\sqrt{d}\\\\}^d$ and then apply the mechanism for the 2-way marginal problem. That does work but it is unclear what the error i...	[ -1, -1, -1, -1, -1, -1, -1, -1, 6, 7, 8, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 3, 4 ]	[ "4BtpOocJBxg", "lMKnkDLSJfX", "Xk_LWa8zMbS", "2ThMVST8Wq3", "mC6-yuQ0c7-", "HR9NEXNKvIu", "fe0mhJOY9JZ", "NE3Ccu3NM_", "nips_2022_ZVuzllOOHS", "nips_2022_ZVuzllOOHS", "nips_2022_ZVuzllOOHS", "nips_2022_ZVuzllOOHS" ]
nips_2022_tHK5ntjp-5K	LION: Latent Point Diffusion Models for 3D Shape Generation	Denoising diffusion models (DDMs) have shown promising results in 3D point cloud synthesis. To advance 3D DDMs and make them useful for digital artists, we require (i) high generation quality, (ii) flexibility for manipulation and applications such as conditional synthesis and shape interpolation, and (iii) the ability...	Accept	This paper proposes a latent point diffusion model, LION, for 3D shape generation. The model builds two denoising diffusion models in the latent spaces of a variational autoencoder. The latent spaces combine a global shape latent representation with a point-structured latent space. Comprehensive experiments are conduc...	train	[ "AGFCoCOUXbF", "Z-c0HikpD3", "1SnmvizqK5X", "fr8AbMwPJ_T", "vaoqfL-FGi", "42-dopnqLI", "efSATmyxu-m", "KVvxm0gGp7H", "dDQRjW9rV2N", "mqKwAsApmjT", "Ogc4trMz9lC", "zWJJutKKt4Z", "TsorGO7i9t", "YW6ROs7TV4", "eF1sj5tD47e", "FmxngAJUJTA", "UbrkT1OPUT", "Ev7OK1CGc8", "S_HLpYr_mOu" ]	[ "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " The rebuttal alleviates my concerns and also answers other reviewers' questions. So I keep my attitude as borderline accept. \n", " After reading the authors' clarifications, I think their comments have resolved most of my concerns. I'm inclined to accept this paper and thus keep my original rating.", " Thank...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 7, 5, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 3, 3, 4 ]	[ "zWJJutKKt4Z", "Ogc4trMz9lC", "vaoqfL-FGi", "nips_2022_tHK5ntjp-5K", "42-dopnqLI", "efSATmyxu-m", "S_HLpYr_mOu", "dDQRjW9rV2N", "Ev7OK1CGc8", "Ogc4trMz9lC", "UbrkT1OPUT", "TsorGO7i9t", "YW6ROs7TV4", "FmxngAJUJTA", "nips_2022_tHK5ntjp-5K", "nips_2022_tHK5ntjp-5K", "nips_2022_tHK5ntjp-...
nips_2022_crFMP5irwzn	Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation	In the past few years, transformers have achieved promising performance on various computer vision tasks. Unfortunately, the immense inference overhead of most existing vision transformers withholds them from being deployed on edge devices such as cell phones and smart watches. Knowledge distillation is a widely used p...	Accept	Four experts in the field reviewed the paper and recommended Borderline Accept, Weak Accept, Accept, and Borderline Accept. The reviewers generally liked the approach, though some commented that it is straightforward. The reviewers' questions about experiments and clarifications were well addressed by the rebuttal. Hen...	train	[ "7XoVK1fCr9C", "mkAA-JgBjbg", "TOEUKWhFKoq", "gpXagSTCwA", "k3dV-Qq6nwk", "mZay8T8hH81", "d-3-M9YMkdu", "xKDm2uFmb1", "SeRKYY242C", "AajF035iLPq", "XdOoL1Gvvqh", "yIH5fmXSF2K", "6iVBqGI4WD", "yhKmzpoc1pY", "1A_wLFZ8axy", "MfL3CtHRz5t", "ZIWxACZ1yGs" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewer ipbi:\n\n\nThanks for your feedback!\n\n\nBest,\n\nPaper3797 Authors", " Thanks for the reviewers' rebuttal, which has solved most of my concerns.\n\nSo I would like to raise my rating from Borderline reject (4) to Borderline accept (6) \n\nBest,", " Dear Reviewer eVwU,\n\n\nThanks again for you...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 6, 7, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 3, 4 ]	[ "mkAA-JgBjbg", "yhKmzpoc1pY", "gpXagSTCwA", "xKDm2uFmb1", "yhKmzpoc1pY", "d-3-M9YMkdu", "XdOoL1Gvvqh", "SeRKYY242C", "ZIWxACZ1yGs", "1A_wLFZ8axy", "MfL3CtHRz5t", "6iVBqGI4WD", "yhKmzpoc1pY", "nips_2022_crFMP5irwzn", "nips_2022_crFMP5irwzn", "nips_2022_crFMP5irwzn", "nips_2022_crFMP5i...
nips_2022_P_eBjUlzlV	On the Limitations of Stochastic Pre-processing Defenses	Defending against adversarial examples remains an open problem. A common belief is that randomness at inference increases the cost of finding adversarial inputs. An example of such a defense is to apply a random transformation to inputs prior to feeding them to the model. In this paper, we empirically and theoretically...	Accept	This paper considers the effectiveness of stochastic preprocessing methods at achieving adversarial robustness. It shows empirically that the common Expectation of Transformations attack is not necessary to break many such defenses, as these defenses are vulnerable to standard PGD attacks when the amount of randomizati...	train	[ "-H0w6Wzd9pc", "O1YQn4q5sW", "tZrpTLf0rm6", "8Yj2uIrRjd0", "v68GFJVNQaB", "yXy_mfn7BnB", "VMMb05-yXFl", "8wpq_06uv_S", "k2srZV3h2EH", "0nagCvU9QZT", "p9jslyzMVGK", "63Cry6fZp8o", "tr7DOUtqQgX", "i1vRufZ61gt", "ubn_atE0RGr", "IuOL07UHv1R", "gUx6_iCNaeV", "nKRRBEEzeem" ]	[ "author", "author", "official_reviewer", "official_reviewer", "author", "author", "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We appreciate the reviewer's detailed review and positive comments. We will incorporate these discussions and additional experimental results into the main paper and provide more details in the appendix.", " We appreciate the reviewer's detailed review and positive comments. We will incorporate these discussion...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4 ]	[ "tZrpTLf0rm6", "8Yj2uIrRjd0", "63Cry6fZp8o", "IuOL07UHv1R", "nKRRBEEzeem", "IuOL07UHv1R", "k2srZV3h2EH", "nips_2022_P_eBjUlzlV", "0nagCvU9QZT", "tr7DOUtqQgX", "nKRRBEEzeem", "nKRRBEEzeem", "gUx6_iCNaeV", "IuOL07UHv1R", "IuOL07UHv1R", "nips_2022_P_eBjUlzlV", "nips_2022_P_eBjUlzlV", ...
nips_2022_caH1x1ZBLDR	Distributionally Robust Optimization with Data Geometry	Distributionally Robust Optimization (DRO) serves as a robust alternative to empirical risk minimization (ERM), which optimizes the worst-case distribution in an uncertainty set typically specified by distance metrics including $f$-divergence and the Wasserstein distance. The metrics defined in the ostensible high dime...	Accept	The paper proposes a novel distributionally robust optimization formulation leveraging data geometry to construct the uncertainty set. After a lengthy discussion and revision process, the reviewers have reached a consensus acceptance recommendation, which I support. Currently the reproducibility checklist (part 3a) st...	train	[ "FgSmng8wexw", "tIp2EvGvYrU", "2LZNJECPt-", "-6QNXzgSTQR", "FXP0PnY_HAj", "1fzyh4tziQB", "ad-x1n85qcr", "mEiDfafqZas", "cG4DwX-5rW4I", "KOmpJ99kv2VW", "VopZSZL8CUS", "5bkYZVz4OAQV", "Tl61RZpCF7_", "tC6kwUS7blv", "PC-pJQudCXr", "uWnaXbdNT87", "3aYAIlBue7c", "vtV6uKnSq8z", "2JH1tYR...	[ "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", ...	[ " I would like to raise my score to 7.", " Thank you for your support! \nWe appreciate your efforts in all the constructive suggestions and discussions that help to improve this paper.", " Thank you for your support! \nThanks to your suggestions, several parts have been improved in the rebuttal revision:\n* We ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 7, 8, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 5 ]	[ "2LZNJECPt-", "-6QNXzgSTQR", "FXP0PnY_HAj", "ad-x1n85qcr", "2JH1tYRRgVt", "mEiDfafqZas", "cG4DwX-5rW4I", "vtV6uKnSq8z", "a9oyQCNiri", "VopZSZL8CUS", "5bkYZVz4OAQV", "Tl61RZpCF7_", "tC6kwUS7blv", "PC-pJQudCXr", "uWnaXbdNT87", "3aYAIlBue7c", "a9oyQCNiri", "eSyFkO2mEPI", "DPIGA9XSRl...
nips_2022_AIqC7F7xV-d	Learning Unified Representations for Multi-Resolution Face Recognition	In this work, we propose Branch-to-Trunk network (BTNet), a novel representation learning method for multi-resolution face recognition. It consists of a trunk network (TNet), namely a unified encoder, and multiple branch networks (BNets), namely resolution adapters. As per the input, a resolution-specific BNet is used ...	Reject	This paper proposes a Branch-to-Trunk network with multiple independent branch networks and a shared trunk network for multi-resolution face recognition. This paper received three detailed reviews. While there are some merits in this work, the reviewers raised many concerns, including 1) inadequate experiments to dem...	train	[ "Qk5G9ocMjJG", "cw6QAf2dLG", "bSuN2m4dPS_", "gFd1m3SQTQ", "IXcuCGpkQod", "3MU94jlfhe", "Vk9dTtzXeHp", "lclDQG6XxEC" ]	[ "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I appreciate the authors for the responses to my concerns. I still have some further concerns regarding to the response.\n\nQ3. It is unclear about the setup of $\\varphi_{mm}$. \nAs $\\varphi_{r}$ has the issue of representation compatibility, it is not suitable for cross-resolution recognition (i.e. see Lin...	[ -1, -1, -1, -1, -1, 4, 3, 5 ]	[ -1, -1, -1, -1, -1, 5, 5, 3 ]	[ "cw6QAf2dLG", "Vk9dTtzXeHp", "lclDQG6XxEC", "3MU94jlfhe", "nips_2022_AIqC7F7xV-d", "nips_2022_AIqC7F7xV-d", "nips_2022_AIqC7F7xV-d", "nips_2022_AIqC7F7xV-d" ]
nips_2022_OmLNqwnZwmY	Falsification before Extrapolation in Causal Effect Estimation	Randomized Controlled Trials (RCTs) represent a gold standard when developing policy guidelines. However, RCTs are often narrow, and lack data on broader populations of interest. Causal effects in these populations are often estimated using observational datasets, which may suffer from unobserved confounding and selec...	Accept	The authors propose an approach for estimating causal effects when both observational and limited experimental data exists. The authors propose falsifying effect estimates from observational data before using the effect estimate on other populations. This is an important idea that may improve reliability of causal infe...	train	[ "7U099W4cs28", "gqjCwBWwCX2", "cFX4Q5vxHEF", "2svv-nQqFB9", "yUKXXe4iSi4A", "7x_oORmX3VA", "R-UtuE6ltxa", "uEwhJBvrtJm", "CH8opepDqJP", "1Rr0rxBMIdG", "Opof4xl0Bi", "jLTqN9O9lGLk", "cL3_3qwEs64", "H11Et8n7KxM", "kBrayJWsRg0", "8CQqW8IxvKU" ]	[ "official_reviewer", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for your response to address my concerns. I adjusted the score accordingly. ", " Thank you again for your review! One of your major concerns appeared to be the lack of a \"real data\" experiment, which we have now provided.\n\nCould you please let us know if this experiment (and our other clarificatio...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 4, 3 ]	[ "7x_oORmX3VA", "H11Et8n7KxM", "cL3_3qwEs64", "yUKXXe4iSi4A", "8CQqW8IxvKU", "kBrayJWsRg0", "H11Et8n7KxM", "cL3_3qwEs64", "1Rr0rxBMIdG", "Opof4xl0Bi", "jLTqN9O9lGLk", "nips_2022_OmLNqwnZwmY", "nips_2022_OmLNqwnZwmY", "nips_2022_OmLNqwnZwmY", "nips_2022_OmLNqwnZwmY", "nips_2022_OmLNqwnZw...
nips_2022_Yay6tHq1Nw	Improving Policy Learning via Language Dynamics Distillation	Recent work has shown that augmenting environments with language descriptions improves policy learning. However, for environments with complex language abstractions, learning how to ground language to observations is difficult due to sparse, delayed rewards. We propose Language Dynamics Distillation (LDD), which pretra...	Accept	This work proposes to learn better representations for language description-based tasks like navigation, via first pretraining a dynamics model on sequences of observations without action labels and using this model to aid RL-based policy learning. Good empirical results are presented and the work has been well-receive...	val	[ "mERFQJcXVbT", "EbjB3ZJQOXI", "mRtx-RQFC46", "qil9cB4a-1B", "qiKVHTuPjI", "3wp-sh0P6J6", "YWW3pjDZcy8", "mQ67FiBRN3C", "nZ0e8CYTJkV", "IgzpDcb5aYR", "81utqpezyf3", "fiQYxzkEttj", "u5Ww-BUGjn_" ]	[ "author", "author", "author", "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " As suggested by ZHyY, we implemented a VAE pretraining baseline, a standard representation learning method for RL, and added its results for Messenger to Figure 6 and RTFM to Figure 5. The intermediate variable for VAE here being the representation just before the policy head. We found that VAE pretraining underp...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 4, 4, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 3, 4 ]	[ "IgzpDcb5aYR", "qiKVHTuPjI", "qiKVHTuPjI", "nips_2022_Yay6tHq1Nw", "3wp-sh0P6J6", "u5Ww-BUGjn_", "fiQYxzkEttj", "81utqpezyf3", "IgzpDcb5aYR", "nips_2022_Yay6tHq1Nw", "nips_2022_Yay6tHq1Nw", "nips_2022_Yay6tHq1Nw", "nips_2022_Yay6tHq1Nw" ]
nips_2022_8UUtKmSRkXE	On Gap-dependent Bounds for Offline Reinforcement Learning	This paper presents a systematic study on gap-dependent sample complexity in offline reinforcement learning. Prior works showed when the density ratio between an optimal policy and the behavior policy is upper bounded (single policy coverage), then the agent can achieve an $O\left(\frac{1}{\epsilon^2}\right)$ rate, whi...	Accept	This paper studies gap-dependent sample complexity in offline tabular RL. The authors show that when there is a gap in the optimal Q-function (and the density ratio between optimal and behavior policies is upper-bounded), the sample complexity can be improved from $O(1/\epsilon^2)$ to $O(1/\epsilon)$ using a pessimisti...	train	[ "QSFW8RJ1Hk", "_N7SEGEXUN2", "i5LCVbNggMP", "-CGVS00YFg5", "BYXG-NPaSSL", "idZ45IIjh7", "96dA-OGy95", "xVQ8uPQ7yA-", "MRsv7RzUXN8", "5BB7NRm5kPC", "e5_vU4JJaen", "dbbMVyVLn_g", "ddUlDxa_Y9e" ]	[ "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for your reply and for adjusting your score!\n\n+ Setting $\\epsilon = \\mathrm{gap}_\\min$ implies finding the optimal policy: Sorry about the confusion. We will emphasize the difference between identifying an optimal policy and a near-optimal policy in the final version.\n+ **Significance of Upper Bo...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 5, 7, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 5, 4, 3 ]	[ "_N7SEGEXUN2", "-CGVS00YFg5", "ddUlDxa_Y9e", "e5_vU4JJaen", "5BB7NRm5kPC", "ddUlDxa_Y9e", "dbbMVyVLn_g", "e5_vU4JJaen", "5BB7NRm5kPC", "nips_2022_8UUtKmSRkXE", "nips_2022_8UUtKmSRkXE", "nips_2022_8UUtKmSRkXE", "nips_2022_8UUtKmSRkXE" ]
nips_2022_bdnZ_1qHLCW	ResQ: A Residual Q Function-based Approach for Multi-Agent Reinforcement Learning Value Factorization	The factorization of state-action value functions for Multi-Agent Reinforcement Learning (MARL) is important. Existing studies are limited by their representation capability, sample efficiency, and approximation error. To address these challenges, we propose, ResQ, a MARL value function factorization method, which can ...	Accept	The paper is for the most part well written and contains both theoretical analyses and a comprehensive empirical study. One of the main initial concerns brought up by various reviewers is that the relation between the proposed method, resQ, and the closely related existing methods Qtran and Qplex is not 100% clear. The...	train	[ "J2Hjnr9KT8g", "te3HHzAEBvpL", "wIva2OZRj5", "k5W0XDKU-j", "sBpzRPg1c1o", "apRy92Ww3qa", "RqpRI3Z83L", "VSWMsUFYuL1", "WBhl6i7hR8C", "-ono5mY3eRI", "7hgCd9_QgaL", "8b1jKJkLpUS", "UyH7MSB-QY", "E_mg3UDz7uP", "12bUQHN_nmJ", "i4Rba3HUE5w", "K4HgraAXO6P" ]	[ "author", "author", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We would like to express our gratitute to the reviewer for this comments which help us improve the quality of this work. \n\nWe have changed the sentence \"Achieving the IGM and the DIGM principles with low approximation errors and high sample efficiency remains an open challenge\" to be *\"Achieving the IGM an...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 7, 7, 4 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 2, 4 ]	[ "sBpzRPg1c1o", "sBpzRPg1c1o", "k5W0XDKU-j", "apRy92Ww3qa", "RqpRI3Z83L", "WBhl6i7hR8C", "VSWMsUFYuL1", "K4HgraAXO6P", "nips_2022_bdnZ_1qHLCW", "7hgCd9_QgaL", "E_mg3UDz7uP", "i4Rba3HUE5w", "12bUQHN_nmJ", "nips_2022_bdnZ_1qHLCW", "nips_2022_bdnZ_1qHLCW", "nips_2022_bdnZ_1qHLCW", "nips_...
nips_2022_u4KagP_FjB	Spartan: Differentiable Sparsity via Regularized Transportation	We present Spartan, a method for training sparse neural network models with a predetermined level of sparsity. Spartan is based on a combination of two techniques: (1) soft top-k masking of low-magnitude parameters via a regularized optimal transportation problem and (2) dual averaging-based parameter updates with hard...	Accept	All reviewers agree that the paper is clearly written and proposes an algorithm which is both novel and efficient. The rebuttal has clarified a number of points, and thereby adressed most of the concerns of the reviewers. The authors are thus strongly encouraged to take into account the comments of the reviewers and t...	train	[ "iUrKZjrcdRh", "Eqz86P6xjH", "zutGpJVkTK", "SX9vDO5EGaD", "n34LPZ4HZo", "NvwEkfz3V7F", "3EAoE7X3Z2Nk", "CHQhN9GdzAag", "5tw4Zgoe69w", "MpniuY1LsAb", "0z-SzKhcPQF", "AKg-6wulFw", "cxZrhQIVPMB", "sfGhMusYpZT", "mU3xynnpOKx", "yhLedqzE9YA" ]	[ "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for the response. I keep my score and vote for weak acceptance.", " We greatly appreciate this and would like to gently remind the reviewer to actually raise the rating to 6 as it still seems to be set at 3.", " I thank the authors for their response.\n\nIt is all clear to me now. The authors have a...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 8, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 3, 3 ]	[ "0z-SzKhcPQF", "zutGpJVkTK", "SX9vDO5EGaD", "n34LPZ4HZo", "AKg-6wulFw", "5tw4Zgoe69w", "nips_2022_u4KagP_FjB", "MpniuY1LsAb", "cxZrhQIVPMB", "sfGhMusYpZT", "mU3xynnpOKx", "yhLedqzE9YA", "nips_2022_u4KagP_FjB", "nips_2022_u4KagP_FjB", "nips_2022_u4KagP_FjB", "nips_2022_u4KagP_FjB" ]
nips_2022_GzESlaXaN04	Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks	We give superpolynomial statistical query (SQ) lower bounds for learning two-hidden-layer ReLU networks with respect to Gaussian inputs in the standard (noise-free) model. No general SQ lower bounds were known for learning ReLU networks of any depth in this setting: previous SQ lower bounds held only for adversarial no...	Accept	This work provides lower bounds in the noise free setting for learning two hidden layer networks in the Gaussian space. Overall it is a fundamental result well within the scope of Neurips, continuing a solid line of work and I cannot see any reason for rejection. The authors have engaged with the reviewers, and have c...	val	[ "DrxgDx3lnP1", "z2NOtlvNWlK", "1fvL7JxoP88", "Fw6vLGOtT2l", "_kDzubyhJm3", "lb-HvYr3wdC", "imJuS5SAwXs" ]	[ "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I am satisfied with the author's response.", " Thanks for the positive feedback! There is indeed a distinction between SQ and SGD, though note that by recent results of Abbe, Sandon, and coauthors, SGD with a suitable architecture + exotic initialization is essentially “P-complete,” so the only way to rule out ...	[ -1, -1, -1, -1, 7, 8, 7 ]	[ -1, -1, -1, -1, 5, 3, 3 ]	[ "z2NOtlvNWlK", "imJuS5SAwXs", "lb-HvYr3wdC", "_kDzubyhJm3", "nips_2022_GzESlaXaN04", "nips_2022_GzESlaXaN04", "nips_2022_GzESlaXaN04" ]
nips_2022_Q9lm8w6JpXi	BILCO: An Efficient Algorithm for Joint Alignment of Time Series	Multiple time series data occur in many real applications and the alignment among them is usually a fundamental step of data analysis. Frequently, these multiple time series are inter-dependent, which provides extra information for the alignment task and this information cannot be well utilized in the conventional pair...	Accept	In this paper, the authors propose an algorithm BILCO for solving graphical time warping, an alignment method for multiple time series data. Overall, the proposed approach is interesting, and all reviewers are positive. Thus, I also vote for acceptance.	val	[ "6I1rQFfaWRP", "embrHrgeRF", "y3SXFnImFTN", "1X9c0rw8vGD", "VVLk4pQHDq8", "OiGhEFCpvnh", "l54AnfsoI_g" ]	[ "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for the response and hope to see the illustrative example in the final version.", " Thank you for your valuable feedback. The major concern was whether the high efficiency of our method is at the expense of alignment accuracy. We would like to take this opportunity to relieve your concern.\n\nFirst, ...	[ -1, -1, -1, -1, 6, 7, 6 ]	[ -1, -1, -1, -1, 4, 1, 4 ]	[ "1X9c0rw8vGD", "l54AnfsoI_g", "OiGhEFCpvnh", "VVLk4pQHDq8", "nips_2022_Q9lm8w6JpXi", "nips_2022_Q9lm8w6JpXi", "nips_2022_Q9lm8w6JpXi" ]
nips_2022_YZ-N-sejjwO	Models Out of Line: A Fourier Lens on Distribution Shift Robustness	Improving the accuracy of deep neural networks on out-of-distribution (OOD) data is critical to an acceptance of deep learning in real world applications. It has been observed that accuracies on in-distribution (ID) versus OOD data follow a linear trend and models that outperform this baseline are exceptionally rare (a...	Accept	All reviewers noted the relevance of the proposed study for the NeurIPS community. They all agreed that the paper is well-motivated, sound, and that the proposed Fourier interpolation is novel. While some reviewers had initial concerns regarding the experimental evaluation, the authors did a great job at improving thei...	test	[ "Tjsd7L30OlF", "P1MZNr8HDcD", "o5ZrVJDFTU3", "y9rCBB_cn1o", "_NE05ZaKOjv", "CjEOjxgio42", "5KhtknN2gRD", "iA-gNq85eFc", "Oqi-yfhfSBm", "LqwgGnxHGGZQ", "Fa21d5ve9sj", "mW-8Q0AiPk", "2EsEX4qUJtp" ]	[ "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks a lot for the authors' responses. After seeing the rebuttal and other reviewers' comments and checking the revised submission, I think my main concerns are well-addressed and I will raise the recommendation to 'weak accept'.", " Thank you all for your time and thoughtful reviews! As the discussion period...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 3 ]	[ "_NE05ZaKOjv", "nips_2022_YZ-N-sejjwO", "CjEOjxgio42", "mW-8Q0AiPk", "mW-8Q0AiPk", "2EsEX4qUJtp", "Fa21d5ve9sj", "Fa21d5ve9sj", "nips_2022_YZ-N-sejjwO", "nips_2022_YZ-N-sejjwO", "nips_2022_YZ-N-sejjwO", "nips_2022_YZ-N-sejjwO", "nips_2022_YZ-N-sejjwO" ]
nips_2022_A0ejsEHQu9w	Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization	Nonsmooth nonconvex optimization problems broadly emerge in machine learning and business decision making, whereas two core challenges impede the development of efficient solution methods with finite-time convergence guarantee: the lack of computationally tractable optimality criterion and the lack of computationally p...	Accept	The authors introduce two derivative-free algorithms for computing the Goldstein stationary points in the context of nonconvex nonsmooth optimization, and show that they enjoy polynomial complexity (in expectation), while the dimension dependence (which is unavoidable in the derivative-free setting) is worse by only an...	train	[ "RsYMIbxhlx1", "MyrGIhpzvBK", "ZfVVhAiuwKL", "bUILwlcefgi", "pIe_eYRUC_B", "2aZjFJc-7v2", "ksJioR-u1p", "dguYxaTiyYu" ]	[ "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for the authors' responses.\n\nI have read all responses and reviews. The authors have solved my concerns. So I keep my score.", " Thank you for your encouraging comments and positive evaluation! We reply to your questions point-by-point below, and will color all relevant revisions in our paper in ${\\co...	[ -1, -1, -1, -1, -1, 7, 6, 7 ]	[ -1, -1, -1, -1, -1, 4, 2, 4 ]	[ "MyrGIhpzvBK", "dguYxaTiyYu", "ksJioR-u1p", "ksJioR-u1p", "2aZjFJc-7v2", "nips_2022_A0ejsEHQu9w", "nips_2022_A0ejsEHQu9w", "nips_2022_A0ejsEHQu9w" ]
nips_2022_ZqgFbZEb8bW	Visual Clues: Bridging Vision and Language Foundations for Image Paragraph Captioning	People say, "A picture is worth a thousand words". Then how can we get the rich information out of the image? We argue that by using visual clues to bridge large pretrained vision foundation models and language models, we can do so without any extra cross-modal training. Thanks to the strong zero-shot capability of fou...	Accept	All three reviewers have voted weak accept to this paper; the authors have engaged well with the reviewers and have improved their paper. I also recommend acceptance.	train	[ "l6e4KnEuh7Z", "NzbZRsFAJG", "mcmOrXFlWWp", "9Ew05muOW1j", "tyipraZCevA", "IRF9JybSAzQ", "emicOQp4QEC", "zR8wsN-4j1oS", "Zyj1579MCBF", "igmv8-tqZ1", "3b83J1IspH", "842cSKG2xv", "86L8AjVgMyC", "W8dGNQqiAnK", "raVOIUU3f6i", "AnC9BY2BOO_", "9PDdsUkyp9A", "5TYyT_N3awm" ]	[ "official_reviewer", "author", "author", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I would like to thank the authors for the rebuttal. About the baseline captioners, you can maybe take baselines in original BLIP paper such as LEMON or even some older models such as AoANet, but given the time frame, it is not needed. Most of my concerns are resolved and I still recommend acceptance for this pape...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 5, 3, 4, 3 ]	[ "842cSKG2xv", "9PDdsUkyp9A", "AnC9BY2BOO_", "raVOIUU3f6i", "IRF9JybSAzQ", "5TYyT_N3awm", "5TYyT_N3awm", "Zyj1579MCBF", "igmv8-tqZ1", "9PDdsUkyp9A", "AnC9BY2BOO_", "raVOIUU3f6i", "nips_2022_ZqgFbZEb8bW", "nips_2022_ZqgFbZEb8bW", "nips_2022_ZqgFbZEb8bW", "nips_2022_ZqgFbZEb8bW", "nips_...
nips_2022_vQzDYi4dPwM	Size and depth of monotone neural networks: interpolation and approximation	Monotone functions and data sets arise in a variety of applications. We study the interpolation problem for monotone data sets: The input is a monotone data set with $n$ points, and the goal is to find a size and depth efficient monotone neural network with \emph{non negative parameters} and threshold units that inter...	Accept	Surprisingly strong result about the expressive power of monotone networks	train	[ "QVrjiiO2XXH", "8kdNcm8VD7l", "D248y5_AZLV", "0QLCnqjAE91", "2kvDTAJPaA", "RmE8svGi4XX" ]	[ "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you very much for your feedback and for raising several important and interesting questions. \n\n- “These are interesting theoretical findings and help us understand the role of depth in deep learning.”\n\nComment: Thank you! Indeed, this is a theoretical paper focused on mathematical questions.\n\n- “...	[ -1, -1, -1, 7, 7, 4 ]	[ -1, -1, -1, 1, 3, 4 ]	[ "RmE8svGi4XX", "2kvDTAJPaA", "0QLCnqjAE91", "nips_2022_vQzDYi4dPwM", "nips_2022_vQzDYi4dPwM", "nips_2022_vQzDYi4dPwM" ]
nips_2022_SHMi1b7sjXk	Test-Time Training with Masked Autoencoders	Test-time training adapts to a new test distribution on the fly by optimizing a model for each test input using self-supervision. In this paper, we use masked autoencoders for this one-sample learning problem. Empirically, our simple method improves generalization on many visual benchmarks for distribution shifts. Theo...	Accept	This paper performs test-time (unsupervised) adaptation to improve generalization performance (e.g., under distribution shift). The reviewer's concerns were mostly about clarification, both in experimentation as well as overall contribution (e.g., concerns about novelty). The discussion was concise and easy to follow, ...	train	[ "d8CKqe6n2Yq", "lr3orbN3a-mA", "Rl8f8-Grvms", "cYKBLYAX_ONG", "48DJtCVAW6yb", "tAEpn3KLb0F", "KM2jZyvp158", "66yvO1dwbE", "Hlnpo31Iwqw", "WWKikAS5Nzm", "ULNCiLay5Ft" ]	[ "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for providing all your comments and updating the paper!\n\nBased on the response to my and other reviewers, I am overall happy with the quality of the work, and increasing my scores to lean towards acceptance.", " Dear ACs and reviewers, \n\nThank you again for the detailed feedback on our work. We hope ...	[ -1, -1, -1, -1, -1, -1, -1, 8, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, 5, 3, 4, 4 ]	[ "48DJtCVAW6yb", "nips_2022_SHMi1b7sjXk", "ULNCiLay5Ft", "WWKikAS5Nzm", "Hlnpo31Iwqw", "66yvO1dwbE", "nips_2022_SHMi1b7sjXk", "nips_2022_SHMi1b7sjXk", "nips_2022_SHMi1b7sjXk", "nips_2022_SHMi1b7sjXk", "nips_2022_SHMi1b7sjXk" ]
nips_2022_o4uFFg9_TpV	Visual Prompting via Image Inpainting	How does one adapt a pre-trained visual model to novel downstream tasks without task-specific finetuning or any model modification? Inspired by prompting in NLP, this paper investigates visual prompting: given input-output image example(s) of a new task at test time and a new input image, the goal is to automatically p...	Accept	The paper discusses a way to use pre-trained models for downstream tasks. Reviewers generally appreciated the paper but had questions regarding baselines, details, dataset, etc. The rebuttal addressed most of these concerns prompting the reviewers to raise their recommendation. However some questions remained (e.g., ht...	test	[ "0Li1merIM5", "xosS56mQJ7o", "zX1tE3bIsMx", "wMnbkVwkKcS", "nOYiCMM0v6", "wPCrVV96hE", "n3t6ZElPx79", "4JrW35Vd14S", "zoOAXGL-roY", "C_HOQ1Tx4JW", "loUu1RsaBx", "reiRXFhTdXT", "drBS-fm3r-", "mK7IwkyFCfG", "fjf4cYrRQKH", "OKdVWUlnpkD", "PFLRw_rmwCZ", "p54Qo0FPIyf", "lS_JXTtMECsQ" ...	[ "author", "official_reviewer", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_rev...	[ " Thank you for the questions and suggestions. As the discussion phase ends today, we will revise the paper to include the missing technical details and the training/inference figure suggested after the discussion period. Additionally, the code will be made publicly available.\n\nQ: **During testing, did you test t...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 6, 6, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 4, 5 ]	[ "nOYiCMM0v6", "zX1tE3bIsMx", "wMnbkVwkKcS", "drBS-fm3r-", "C_HOQ1Tx4JW", "mK7IwkyFCfG", "mK7IwkyFCfG", "loUu1RsaBx", "nips_2022_o4uFFg9_TpV", "lS_JXTtMECsQ", "reiRXFhTdXT", "p54Qo0FPIyf", "PFLRw_rmwCZ", "OKdVWUlnpkD", "nips_2022_o4uFFg9_TpV", "nips_2022_o4uFFg9_TpV", "nips_2022_o4uFF...
nips_2022_sBrS3M5lT2w	Global Convergence and Stability of Stochastic Gradient Descent	In machine learning, stochastic gradient descent (SGD) is widely deployed to train models using highly non-convex objectives with equally complex noise models. Unfortunately, SGD theory often makes restrictive assumptions that fail to capture the non-convexity of real problems, and almost entirely ignore the complex no...	Accept	This paper analyzes the asymptotic convergence behavior of SGD on the class of locally Hölder continuous functions, by generalizing the technique and results of Patel [2021]. The paper extends and generalizes prior SGD analyses that were conducted under the assertion that certain conditions (e.g. smoothness or continui...	train	[ "O2OicW0ZLFE", "ukv3Y5rqi6", "0FReLw9wwmR", "HTMCPe2LI-jV", "M67HB-LgoJT", "RmUXd1teTLm", "11DG6LkUGBY", "XwP89UB_z0", "XNZE8oqObG1", "XgwwNMa23GC", "I_B_k9JNgaZ", "GevNcbgzdkv", "b3LV9KWZxMs", "byr-1ZcQa9" ]	[ "official_reviewer", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear authors,\n\nthank you for the detailed response and the clarification. I may have underestimated the difficulty of some technical details. I updated my score. This may change again during the discussion phase.\n\nBest regards,\n\nReviewer JXLB", " thank you for your answer which mostly clears up the questi...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 5, 7, 5, 5 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 2, 2, 3 ]	[ "M67HB-LgoJT", "11DG6LkUGBY", "HTMCPe2LI-jV", "RmUXd1teTLm", "byr-1ZcQa9", "b3LV9KWZxMs", "GevNcbgzdkv", "I_B_k9JNgaZ", "XgwwNMa23GC", "nips_2022_sBrS3M5lT2w", "nips_2022_sBrS3M5lT2w", "nips_2022_sBrS3M5lT2w", "nips_2022_sBrS3M5lT2w", "nips_2022_sBrS3M5lT2w" ]
nips_2022_qj-_HnxQxB	Functional Indirection Neural Estimator for Better Out-of-distribution Generalization	The capacity to achieve out-of-distribution (OOD) generalization is a hallmark of human intelligence and yet remains out of reach for machines. This remarkable capability has been attributed to our abilities to make conceptual abstraction and analogy, and to a mechanism known as indirection, which binds two representat...	Accept	This paper tackles OOD generalisation through a mechanism for analogy-making in functional spaces rather than the data space. It involves construction of a functional framework that maps inputs to outputs---by abstracting the transformation between inputs and outputs through a separate (hyper)network which provides the...	train	[ "mVN4bC131bK", "H-pFzss_ahx", "Yt2ZOKcurdf", "4ED3Na8nC6", "1nwv-2EIflv", "RhsZuSkEZv3", "JqP5oIrk1Q2", "LQTFhq0IHCt", "s6DLck9nQ_2", "KEcjMZU5PJ" ]	[ "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for your detailed response. The authors have addressed most of my concerns, so I would like to raise my score from 5 to 6.\n\n", " We thank the reviewer for your detailed and insightful comments. We have updated our manuscript and would like to address the reviewer's concerns as follows: \n\n• “Evaluatio...	[ -1, -1, -1, -1, -1, -1, 3, 6, 6, 5 ]	[ -1, -1, -1, -1, -1, -1, 5, 4, 4, 4 ]	[ "Yt2ZOKcurdf", "KEcjMZU5PJ", "s6DLck9nQ_2", "1nwv-2EIflv", "LQTFhq0IHCt", "JqP5oIrk1Q2", "nips_2022_qj-_HnxQxB", "nips_2022_qj-_HnxQxB", "nips_2022_qj-_HnxQxB", "nips_2022_qj-_HnxQxB" ]
nips_2022_n0dD3d54Wgf	SparCL: Sparse Continual Learning on the Edge	Existing work in continual learning (CL) focuses on mitigating catastrophic forgetting, i.e., model performance deterioration on past tasks when learning a new task. However, the training efficiency of a CL system is under-investigated, which limits the real-world application of CL systems under resource-limited scenar...	Accept	This paper introduces a new continual learning scheme whose efficiency and effectiveness are achieved through three key components that encourage sparse network weight connection, replay buffer selection, and sparse gradient truncation. After the author-review discussion phase, a majority of reviewer suggest acceptance...	train	[ "K-rt-3DDzox", "HLJf7OdNN8Y", "xoM9xsONIWK", "fRHsz3wj5y", "-YNdTehNVN", "6962aArzV6C", "LyZZPlw_vIT", "Aa2o4N8oDjF", "ZF8o0E6jlKG", "QN-C6-H98K1", "Sf0jIoZ0XoX", "vMHik1qGe-A", "7q-fILC-eIa", "fUSnrOeeo7L", "pLcSYRuaDsG", "0pQOI7O9EpI2", "2BM3ldFnnb-", "8T1I-3103Lz", "hI5fvrY_8c...	[ "author", "author", "author", "author", "author", "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear Reviewer g1tN,\n\nThank you very much for spending time reviewing our paper. Since the discussion will end very soon, we sincerely hope that you have found time to check our detailed response to your previous questions/comments. If you have any further questions, please feel free to let us know. We will try ...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 5, 7, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 5, 4, 4 ]	[ "8T1I-3103Lz", "2BM3ldFnnb-", "gkGZ8Lfgd9-", "8T1I-3103Lz", "2BM3ldFnnb-", "LyZZPlw_vIT", "pLcSYRuaDsG", "8T1I-3103Lz", "8T1I-3103Lz", "8T1I-3103Lz", "8T1I-3103Lz", "2BM3ldFnnb-", "2BM3ldFnnb-", "hI5fvrY_8cm", "hI5fvrY_8cm", "gkGZ8Lfgd9-", "nips_2022_n0dD3d54Wgf", "nips_2022_n0dD3d...
nips_2022_u3vEuRr08MT	Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models	Despite their wide adoption, the underlying training and memorization dynamics of very large language models is not well understood. We empirically study exact memorization in causal and masked language modeling, across model sizes and throughout the training process. We measure the effects of dataset size, learning ra...	Accept	This paper studies the underlying training and memorization dynamics of very large language models. The main take aways are that larger-sized language models memorize training data faster, and that this memorization happens before the overfitting of language modeling. Tokens with certain part-of-speech tags (nouns, num...	train	[ "3EPQHFVUWy", "O3PHX3RTT0c", "OWqzCZKAdDM", "0N8mf0lZhfV", "otgTOcycDSz", "dpsFVNDmzZ", "HIj7jGKI3N", "jmta3fzebhN", "WYSU_XYzkSD", "RvyGE5LIJxo", "7IKWxwp0J4", "JEhui3saOMx", "eMdL3Kgp2F6" ]	[ "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for providing a detailed response to my questions. I'm glad that most of my questions are answered, and I'm happy to improve my original evaluation.", " \n*W7: I'm not convinced by the experiments that consider memorization relationship with overfitting as though these are two separate phenomena. What...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 7, 7, 8 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 4, 4, 4 ]	[ "jmta3fzebhN", "OWqzCZKAdDM", "0N8mf0lZhfV", "otgTOcycDSz", "dpsFVNDmzZ", "RvyGE5LIJxo", "7IKWxwp0J4", "JEhui3saOMx", "eMdL3Kgp2F6", "nips_2022_u3vEuRr08MT", "nips_2022_u3vEuRr08MT", "nips_2022_u3vEuRr08MT", "nips_2022_u3vEuRr08MT" ]
nips_2022_Q38D6xxrKHe	High-dimensional limit theorems for SGD: Effective dynamics and critical scaling	We study the scaling limits of stochastic gradient descent (SGD) with constant step-size in the high-dimensional regime. We prove limit theorems for the trajectories of summary statistics (i.e., finite-dimensional functions) of SGD as the dimension goes to infinity. Our approach allows one to choose the summary statist...	Accept	The paper is quite interesting and rigorous, with intriguing conclusions. The rebuttal also addressed all the major concerns -- mostly technical clarity. I congratulate the authors for the nice work and recommend an acceptance for the paper.	val	[ "v-UEO1fdgdk", "KNAAMa6mEOG", "W6_OImRsT0", "EQPOx0cWBHGd", "KNphMyiGCrD", "PjxyOZAkOb4", "J5Jy9BRyBe4", "5z0nsfuxNdKD", "-X6ujiQW4Nh", "kMYMxgSPtl8", "c8Wb6XIMxsO", "vVVxM2OR6HZ", "gn5Ue-aCCKv" ]	[ "official_reviewer", "official_reviewer", "official_reviewer", "author", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Dear authors,\n\nI really appreciate the time you took to answer my questions and clarify certain points. I was also confused by your answer to my question about the gradient-like form of the drift and the bias term as reviewer hQ5R pointed out. But the authors clarified this issue quite well for me. I would also...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 7, 8 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 4 ]	[ "kMYMxgSPtl8", "EQPOx0cWBHGd", "KNphMyiGCrD", "PjxyOZAkOb4", "J5Jy9BRyBe4", "-X6ujiQW4Nh", "kMYMxgSPtl8", "gn5Ue-aCCKv", "vVVxM2OR6HZ", "c8Wb6XIMxsO", "nips_2022_Q38D6xxrKHe", "nips_2022_Q38D6xxrKHe", "nips_2022_Q38D6xxrKHe" ]
nips_2022_HQDvPsdXS-F	Neur2SP: Neural Two-Stage Stochastic Programming	Stochastic Programming is a powerful modeling framework for decision-making under uncertainty. In this work, we tackle two-stage stochastic programs (2SPs), the most widely used class of stochastic programming models. Solving 2SPs exactly requires optimizing over an expected value function that is computationally intra...	Accept	In this paper, the authors proposed to use learning with neural network to amortize the cost in the two-stage optimization problems. The authors tested the algorithms on several problems, demonstrating the advantages of the proposed algorithm empirically. Most of the reviewers think this work is interesting, although...	train	[ "0nLus52PdJ8", "p47c0zXtrOG", "jAzd8xWPhW", "MmEjmhLXXA", "ReMDdGe2oUT", "46mGr8ukJIm", "Ue-Z7sePz9M", "u-1Fnmrgo7S", "Bgl7ngSI19b", "d2zcM9eCdKo", "ps6pGcxv5hD", "rYtXjbEct_", "bF9D59g7QKs", "h-AwFrtU9s" ]	[ "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for the response. I appreciate that the camera-ready version would be more upfront about the training and data collection time, given that this is not the typical case where you would generalize for a wide set of instances.\n\nI understand the reasons not to include stronger baselines. This is however i...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 7, 8, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 4 ]	[ "Bgl7ngSI19b", "jAzd8xWPhW", "Ue-Z7sePz9M", "ReMDdGe2oUT", "d2zcM9eCdKo", "nips_2022_HQDvPsdXS-F", "h-AwFrtU9s", "bF9D59g7QKs", "rYtXjbEct_", "ps6pGcxv5hD", "nips_2022_HQDvPsdXS-F", "nips_2022_HQDvPsdXS-F", "nips_2022_HQDvPsdXS-F", "nips_2022_HQDvPsdXS-F" ]
nips_2022_S2Awu3Zn04v	Approximate Value Equivalence	Model-based reinforcement learning agents must make compromises about which aspects of the environment their models should capture. The value equivalence (VE) principle posits that these compromises should be made considering the model's eventual use in value-based planning. Given sets of functions and policies, a mod...	Accept	A key discussion point in the rebuttal phase was the practical use of the proposed bounds, which two of the three reviewers brought up. The authors in response added an additional section (Section 6) and experiment to address this concern. While some concerns regarding the practical use of these bounds remain, the auth...	train	[ "lAIEovDK53n", "oOydCCKHbT", "Pwd1KwRwlqUO", "5Lr23hsBak", "zGv2iJxJ8J", "I75FB7UgET5", "h10qktEe2SH", "Img1fRIDdC", "zl23edjuRe6", "R_4G76nCgv" ]	[ "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I thank the authors for the response to the issues I raised. Some additional notes:\n\n* Regarding providing some intuition on how the different propositions are established, I think that a 1-liner hint on how the result is established would be helpful in all, but - personally - I found that Propositions 2 and 3 ...	[ -1, -1, -1, -1, -1, -1, -1, 6, 7, 4 ]	[ -1, -1, -1, -1, -1, -1, -1, 2, 4, 3 ]	[ "I75FB7UgET5", "zGv2iJxJ8J", "nips_2022_S2Awu3Zn04v", "R_4G76nCgv", "zl23edjuRe6", "Img1fRIDdC", "nips_2022_S2Awu3Zn04v", "nips_2022_S2Awu3Zn04v", "nips_2022_S2Awu3Zn04v", "nips_2022_S2Awu3Zn04v" ]
nips_2022_gQBetxnU4Lk	Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding	Learning-based methods have shown promising performance for accelerating motion planning, but mostly in the setting of static environments. For the more challenging problem of planning in dynamic environments, such as multi-arm assembly tasks and human-robot interaction, motion planners need to consider the trajectorie...	Accept	Robot motion planing in dynamic environments remains a significant problem. All reviewers consistently agree that the suggest GNN approach in this paper has useful merits, is of general interest, and that the paper is above the publication threshold. Detailed comments of the reviewers provide a good source for some fin...	val	[ "sB58zPeVdsg", "QEQIkC70Pxy5", "m6xrguRAP5S", "ZwJ2-642jz", "OMgDUy4krZ", "ec_Jfr12KLG", "im6mbLdU66A", "YUkI6rNpXjF", "Se2eLywhK4I", "eCKkci9wMmI" ]	[ "author", "official_reviewer", "author", "official_reviewer", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " We thank the reviewer for the crucial questions and carefully reading our response! As also discussed in Appendix D, we think the topics on planning in more challenging environments and problems will be a great direction for our future work.", " Thanks for the response! My questions are addressed.\n\nIt's inter...	[ -1, -1, -1, -1, -1, -1, -1, 6, 6, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, 3, 4, 4 ]	[ "QEQIkC70Pxy5", "im6mbLdU66A", "ZwJ2-642jz", "ec_Jfr12KLG", "eCKkci9wMmI", "Se2eLywhK4I", "YUkI6rNpXjF", "nips_2022_gQBetxnU4Lk", "nips_2022_gQBetxnU4Lk", "nips_2022_gQBetxnU4Lk" ]
nips_2022_IJNDyqdRF0m	Decomposing NeRF for Editing via Feature Field Distillation	Emerging neural radiance fields (NeRF) are a promising scene representation for computer graphics, enabling high-quality 3D reconstruction and novel view synthesis from image observations. However, editing a scene represented by a NeRF is challenging, as the underlying connectionist representations such as MLPs or voxe...	Accept	The paper proposes an approach for manipulating 3d scenes represented with implicit neural representations (NeRF-like), via distilling 2D feature extractors into a 3D feature field. The method shows convincing qualitative results on scene editing and promising quantitative results on semantic segmentation. All review...	train	[ "QJ_9bJe_Jzj", "pHy6RHJgm9r", "WaXoIDcNdB-", "b_5taPMlLeHB", "JybWGWzUz6A", "yQMEyvQj45T", "U1I2Gh2brQ5", "bsTf83a8zk", "100f6-fmQ4_", "D96WSsH2zgR", "WixSHEi4EsX", "rVs6u0h7if-", "80yT57QNMbf" ]	[ "official_reviewer", "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for the response. I am happy to keep my original score and suggest acceptance.\n\n", " Thanks for the Authors' response, which I believe clarifies many concerns of mine and other reviewers. Without additional concerns from other reviewers, I would keep my original score and suggest acceptance.", " D...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 5, 6, 7 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 5, 4, 3 ]	[ "yQMEyvQj45T", "WaXoIDcNdB-", "80yT57QNMbf", "rVs6u0h7if-", "WixSHEi4EsX", "D96WSsH2zgR", "bsTf83a8zk", "100f6-fmQ4_", "nips_2022_IJNDyqdRF0m", "nips_2022_IJNDyqdRF0m", "nips_2022_IJNDyqdRF0m", "nips_2022_IJNDyqdRF0m", "nips_2022_IJNDyqdRF0m" ]
nips_2022_AXDNM76T1nc	Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos	Pretraining on noisy, internet-scale datasets has been heavily studied as a technique for training models with broad, general capabilities for text, images, and other modalities. However, for many sequential decision domains such as robotics, video games, and computer use, publicly available data does not contain the l...	Accept	The authors have introduced Video Pre-Training (VPT), a semi-supervised learning approach that allows relatively small volumes of labeled data to train an inverse-dynamics model that is subsequently applied to predict the action labels associated with a far larger, unlabeled dataset. They then train an agent in a super...	val	[ "O3fH2iikdrq", "ugmzTcQH_P", "1Igt61qqri9", "5yP34P1JEx", "t18CZqD-6qf", "sTrUMktaBh9", "155ufQFqd5L", "eEBH3m1uqgGH", "6t0iuUM7bQW", "1NpQoYJQ5TD", "EkPy2nZxVB0", "1xlO8Wd1BVe", "iB75rCF-ZsQ", "wSuC-co5iWu0" ]	[ "official_reviewer", "author", "official_reviewer", "author", "official_reviewer", "official_reviewer", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thanks for the answers to my questions, they are all satisfactory! \n\nOne minor suggestion: could you please discuss relations to a recent concurrent work, MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge (https://arxiv.org/abs/2206.08853)? It seems very complementary, and readers from...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 8, 9, 9, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 4, 4, 4, 5 ]	[ "1NpQoYJQ5TD", "1Igt61qqri9", "5yP34P1JEx", "sTrUMktaBh9", "6t0iuUM7bQW", "155ufQFqd5L", "wSuC-co5iWu0", "iB75rCF-ZsQ", "1xlO8Wd1BVe", "EkPy2nZxVB0", "nips_2022_AXDNM76T1nc", "nips_2022_AXDNM76T1nc", "nips_2022_AXDNM76T1nc", "nips_2022_AXDNM76T1nc" ]
nips_2022_se2oxj-6Nz	Rethinking Image Restoration for Object Detection	Although image restoration has achieved significant progress, its potential to assist object detectors in adverse imaging conditions lacks enough attention. It is reported that the existing image restoration methods cannot improve the object detector performance and sometimes even reduce the detection performance. To a...	Accept	In this paper, the authors provide an interesting formulation of an adversarial attack that can directly help object detector training in the presence of various degradations. This is a departure from the usual formulation of restoration, followed by detector training. I liked the initial derivation which is elegant an...	train	[ "fuwsUiydg5-", "kpzunnZWqbj", "vdpV8Aq1Kws", "6wqFuXzs6PP", "LZti962MB2V", "3Yc2UowHJ-W", "jG30-yMoGOB", "QgGGjBgKvC", "ASxWbk6izDi", "QH5P1a8fRw1", "4uGrwtiFq-w" ]	[ "author", "official_reviewer", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " Thank you for your issue. But we still want to emphasize the universality of our method. Our algorithm can extend to most of the restoration networks and detection networks. For most restoration tasks, e.g., haze removal (Table 2), low-light enhancement (Table 3), and the case with multiple degradations (the tabl...	[ -1, -1, -1, -1, -1, -1, -1, -1, 5, 4, 8 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, 4, 3, 5 ]	[ "kpzunnZWqbj", "jG30-yMoGOB", "nips_2022_se2oxj-6Nz", "ASxWbk6izDi", "4uGrwtiFq-w", "QH5P1a8fRw1", "QH5P1a8fRw1", "ASxWbk6izDi", "nips_2022_se2oxj-6Nz", "nips_2022_se2oxj-6Nz", "nips_2022_se2oxj-6Nz" ]
nips_2022_Oq2bdIQQOIZ	On Privacy and Personalization in Cross-Silo Federated Learning	While the application of differential privacy (DP) has been well-studied in cross-device federated learning (FL), there is a lack of work considering DP and its implications for cross-silo FL, a setting characterized by a limited number of clients each containing many data subjects. In cross-silo FL, usual notions of c...	Accept	The paper presents an analysis of item-level or sample-level DP with personalization in cross-silo federated learning. The reviews are strongly divided with two recommending acceptance and two recommending rejection. After reading all the reviews and the paper, I find the argument of the "accept" side stronger. Whil...	train	[ "LwzUOEemOtZ", "cVsqGO-RX0", "UhgUTt0t1c0", "uGnVZ4MNHl6", "ykcCtAZZID", "n7RKftXKEQ8", "20rH0GEI63z", "QkslmvkMirY", "rMtUPaS_fOR", "cOZla78PwcO", "U2m767jL7Ku", "G_dq5I2DaEX", "mPKqadLVyHC", "6D3pMlYY4H0", "r0B9lsjVU_k" ]	[ "official_reviewer", "author", "author", "author", "author", "author", "author", "author", "author", "author", "author", "official_reviewer", "official_reviewer", "official_reviewer", "official_reviewer" ]	[ " I tend to agree with the authors that the main weakness I mentioned (discussion and claims about DP) is due to writing clarity, and trust that the authors will fix these issues for the next version of the paper. I will therefore raise my score to recommend accepting the paper: I think the main point about the nee...	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 7, 4, 3, 6 ]	[ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 3, 3, 4, 4 ]	[ "uGnVZ4MNHl6", "nips_2022_Oq2bdIQQOIZ", "nips_2022_Oq2bdIQQOIZ", "r0B9lsjVU_k", "r0B9lsjVU_k", "6D3pMlYY4H0", "6D3pMlYY4H0", "G_dq5I2DaEX", "nips_2022_Oq2bdIQQOIZ", "mPKqadLVyHC", "mPKqadLVyHC", "nips_2022_Oq2bdIQQOIZ", "nips_2022_Oq2bdIQQOIZ", "nips_2022_Oq2bdIQQOIZ", "nips_2022_Oq2bdIQ...

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.