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d3580738 | Deep learning methods have achieved high performance in sound recognition tasks. Deciding how to feed the training data is important for further performance improvement. We propose a novel learning method for deep sound recognition: Between-Class learning (BC learning). Our strategy is to learn a discriminative feature space by recognizing the between-class sounds as between-class sounds. We generate between-class sounds by mixing two sounds belonging to different classes with a random ratio. We then input the mixed sound to the model and train the model to output the mixing ratio. The advantages of BC learning are not limited only to the increase in variation of the training data; BC learning leads to an enlargement of Fisher's criterion in the feature space and a regularization of the positional relationship among the feature distributions of the classes. The experimental results show that BC learning improves the performance on various sound recognition networks, datasets, and data augmentation schemes, in which BC learning proves to be always beneficial. Furthermore, we construct a new deep sound recognition network (EnvNet-v2) and train it with BC learning. As a result, we achieved a performance surpasses the human level. | Under review as a conference paper at ICLR 2018 LEARNING FROM BETWEEN-CLASS EXAMPLES FOR DEEP SOUND RECOGNITION |
d44132187 | Regularized Nonlinear Acceleration (RNA) can improve the rate of convergence of many optimization schemes such as gradient descent, SAGA or SVRG, estimating the optimum using a nonlinear average of past iterates. Until now, its analysis was limited to convex problems, but empirical observations show that RNA may be extended to a broader setting. Here, we investigate the benefits of nonlinear acceleration when applied to the training of neural networks, in particular for the task of image recognition on the CIFAR10 and ImageNet data sets. In our experiments, with minimal modifications to existing frameworks, RNA speeds up convergence and improves testing error on standard CNNs. | NONLINEAR ACCELERATION OF CNNS |
d252683180 | Training graph neural networks (GNNs) on large graphs is complex and extremely time consuming. This is attributed to overheads caused by sparse matrix multiplication, which are sidestepped when training multi-layer perceptrons (MLPs) with only node features. MLPs, by ignoring graph context, are simple and faster for graph data, however they usually sacrifice prediction accuracy, limiting their applications for graph data. We observe that for most message passing-based GNNs, we can trivially derive an analog MLP (we call this a PeerMLP) with an equivalent weight space, by setting the trainable parameters with the same shapes, making us curious about how do GNNs using weights from a fully trained PeerMLP perform? Surprisingly, we find that GNNs initialized with such weights significantly outperform their PeerMLPs, motivating us to use PeerMLP training as a precursor, initialization step to GNN training. To this end, we propose an embarrassingly simple, yet hugely effective initialization method for GNN training acceleration, called MLPInit. Our extensive experiments on multiple large-scale graph datasets with diverse GNN architectures validate that MLPInit can accelerate the training of GNNs (up to 33× speedup on OGB-products) and often improve prediction performance (e.g., up to 7.97% improvement for GraphSAGE across 7 datasets for node classification, and up to 17.81% improvement across 4 datasets for link prediction on metric Hits@10). The code is available at https://github.com/snapresearch/MLPInit-for-GNNs. | MLPINIT: EMBARRASSINGLY SIMPLE GNN TRAIN- ING ACCELERATION WITH MLP INITIALIZATION |
d257050828 | Recently, great success has been made in learning visual representations from text supervision, facilitating the emergence of text-supervised semantic segmentation. However, existing works focus on pixel grouping and cross-modal semantic alignment, while ignoring the correspondence among multiple augmented views of the same image. To overcome such limitation, we propose multi-View Consistent learning (ViewCo) for text-supervised semantic segmentation. Specifically, we first propose text-to-views consistency modeling to learn correspondence for multiple views of the same input image. Additionally, we propose cross-view segmentation consistency modeling to address the ambiguity issue of text supervision by contrasting the segment features of Siamese visual encoders. The text-to-views consistency benefits the dense assignment of the visual features by encouraging different crops to align with the same text, while the cross-view segmentation consistency modeling provides additional self-supervision, overcoming the limitation of ambiguous text supervision for segmentation masks. Trained with large-scale image-text data, our model can directly segment objects of arbitrary categories in a zero-shot manner. Extensive experiments show that ViewCo outperforms stateof-the-art methods on average by up to 2.9%, 1.6%, and 2.4% mIoU on PASCAL | VIEWCO: DISCOVERING TEXT-SUPERVISED SEGMEN- TATION MASKS VIA MULTI-VIEW SEMANTIC CONSIS- TENCY |
d258426317 | Recently, transformers have shown strong ability as visual feature extractors, surpassing traditional convolution-based models in various scenarios. However, the success of vision transformers largely owes to their capacity to accommodate numerous parameters. As a result, new challenges for adapting a well-trained transformer to downstream tasks arise. On the one hand, classic fine-tuning tunes all parameters in a huge model for every downstream task and thus easily falls into an overfitting situation, leading to inferior performance. On the other hand, on resource-limited devices, fine-tuning stores a full copy of all parameters and thus is usually impracticable for the shortage of storage space. However, few works have focused on how to efficiently and effectively transfer knowledge in a vision transformer. Existing methods did not dive into the properties of visual features, leading to inferior performance. Moreover, some of them bring heavy inference cost though benefiting storage. To tackle these problems, we propose consolidator to achieve efficient transfer learning for large vision models. Our consolidator modifies the pre-trained model with the addition of a small set of tunable parameters to temporarily store the task-specific knowledge while freezing the backbone model during adaptation. Motivated by the success of group-wise convolution, we adopt grouped connections across the features extracted by fully connected layers to construct tunable parts in a consolidator. To further enhance the model's capacity to transfer knowledge under a constrained storage budget and keep inference efficient, we consolidate the parameters in two stages: 1. between adaptation and storage, and 2. between loading and inference. On a series of downstream visual tasks, our consolidator can reach up to 7.56 better accuracy than full finetuning with merely 0.35% parameters, and outperform state-of-the-art parameterefficient tuning methods by a clear margin. Code is available at github.Recently, transformer architectures originated from natural language processing (NLP) (Vaswani et al., 2017) demonstrate considerable capacity in computer vision(Dosovitskiy et al., 2020;Touvron et al., 2021;Liu et al., 2021b). Vision transformers, along with traditional convolutional neural networks (CNNs)(Krizhevsky et al., 2012;He et al., 2016;Simonyan & Zisserman, 2014), are widely used as feature extractors to generate strong and general visual representations via deriving knowledge from massive images. Thanks to the abundant information in such representations, we can adapt the pre-trained models to downstream tasks by a simple fine-tuning strategy.However, fine-tuning is not a good solution for adaptation. As is well known, the scale of vision models grows faster and faster in recent years. On the one hand, fine-tuning which tunes all parameters in such a huge model easily falls into an overfitting situation, leading to inferior performance. On the other hand, fine-tuning inflicts heavy storage burdens. Since fine-tuning intensively tunes all parameters, it maintains a full copy of the model's parameters for each task. Therefore, fine-tuning can cause a huge storage burden when there are many tasks to be adapted, resulting in impracticality in real-world scenarios, especially in resource-constrained situations, e.g., embedded systems. † Corresponding authors. 1 arXiv:2305.00603v1 [cs.CV] 30 Apr 2023Published as a conference paper at ICLR 2023 Efforts have been made to improve the performance as well as reduce the storage overhead of finetuning. For example, adapter(Houlsby et al., 2019;Karimi Mahabadi et al., 2021), prompt tuning(Li & Liang, 2021;Lester et al., 2021;Zhou et al., 2021)andLoRA (Hu et al., 2021)inject tunable parameters and freeze the backbone during adaptation. In the vision field, VPT (Jia et al., 2022) directly leverage learnable prompts, AdaptFormer (Chen et al., 2022) adopts parallel adapter, NOAH (Zhang et al., 2022) searches for the optimal combinations of the three representative modules, i.e., adapter, LoRA, and VPT, and SSF (Lian et al., 2022b) use additional scaling and shifting parameters for adaptation. Despite their acceptable performance, existing methods suffer from two common conflicts: 1. trade-off between the inference efficiency and the adaptation performance, and 2. trade-off between the adaptation performance and the number of stored parameters. Previous works(Houlsby et al., 2019)show that introducing more tunable parameters can achieve more fruitful results. However, extra parameters can bring significantly larger computation and storage cost, resulting in low inference efficiency and more storage space. Therefore, one essential question is raised: can we design a module that can share the same inference cost as an ordinary model while enjoying superior capacity against existing methods?In this paper, we propose a generic module, dubbed consolidator, to tackle the aforementioned issues. The proposed consolidator is designed as a mergeable adapter that accompanies the fully connected (FC) layer in the vision models. Specifically, to enrich the model capacity under a limited parameter budget, we take inspiration from the success of group-wise convolution(Howard et al., 2017;Ma et al., 2018;and build our consolidator as grouped connected (GC) layers. To enhance the flexibility, we further reorder channels for each group connection, followed by a droppath regularizer. Benefiting from the inference-time linearity of GC, channel reorder, and droppath operations, the proposed consolidator can be perfectly consolidated into the original FC layer of a vision model, leading to no extra inference cost. | CONSOLIDATOR: MERGEABLE ADAPTER WITH GROUPED CONNECTIONS FOR VISUAL ADAPTATION |
d534043 | A critical component to enabling intelligent reasoning in partially observable environments is memory. Despite this importance, Deep Reinforcement Learning (DRL) agents have so far used relatively simple memory architectures, with the main methods to overcome partial observability being either a temporal convolution over the past k frames or an LSTM layer. More recent work(Oh et al., 2016)has went beyond these architectures by using memory networks which can allow more sophisticated addressing schemes over the past k frames. But even these architectures are unsatisfactory due to the reason that they are limited to only remembering information from the last k frames. In this paper, we develop a memory system with an adaptable write operator that is customized to the sorts of 3D environments that DRL agents typically interact with. This architecture, called the Neural Map, uses a spatially structured 2D memory image to learn to store arbitrary information about the environment over long time lags. We demonstrate empirically that the Neural Map surpasses previous DRL memories on a set of challenging 2D and 3D maze environments and show that it is capable of generalizing to environments that were not seen during training. | NEURAL MAP: STRUCTURED MEMORY FOR DEEP RE- INFORCEMENT LEARNING |
d257427351 | Despite their success with unstructured data, deep neural networks are not yet a panacea for structured tabular data. In the tabular domain, their efficiency crucially relies on various forms of regularization to prevent overfitting and provide strong generalization performance. Existing regularization techniques include broad modelling decisions such as choice of architecture, loss functions, and optimization methods. In this work, we introduce Tabular Neural Gradient Orthogonalization and Specialization (TANGOS), a novel framework for regularization in the tabular setting built on latent unit attributions. The gradient attribution of an activation with respect to a given input feature suggests how the neuron attends to that feature, and is often employed to interpret the predictions of deep networks. In TANGOS, we take a different approach and incorporate neuron attributions directly into training to encourage orthogonalization and specialization of latent attributions in a fully-connected network. Our regularizer encourages neurons to focus on sparse, non-overlapping input features and results in a set of diverse and specialized latent units. In the tabular domain, we demonstrate that our approach can lead to improved out-of-sample generalization performance, outperforming other popular regularization methods. We provide insight into why our regularizer is effective and demonstrate that TANGOS can be applied jointly with existing methods to achieve even greater generalization performance. Figure 1: TANGOS encourages specialization and orthogonalization. TANGOS penalizes neuron attributions during training. Here, indicates strong positive attribution and indicates strong negative attribution, while interpolating colors reflect weaker attributions. Neurons are regularized to be specialized (attend to sparser features) and orthogonal (attend to non-overlapping features). | TANGOS: REGULARIZING TABULAR NEURAL NET- WORKS THROUGH GRADIENT ORTHOGONALIZATION AND SPECIALIZATION |
d406912 | Humans can understand and produce new utterances effortlessly, thanks to their systematic compositional skills. Once a person learns the meaning of a new verb "dax," he or she can immediately understand the meaning of "dax twice" or "sing and dax." In this paper, we introduce the SCAN domain, consisting of a set of simple compositional navigation commands paired with the corresponding action sequences. We then test the zero-shot generalization capabilities of a variety of recurrent neural networks (RNNs) trained on SCAN with sequence-to-sequence methods. We find that RNNs can generalize well when the differences between training and test commands are small, so that they can apply "mix-and-match" strategies to solve the task. However, when generalization requires systematic compositional skills (as in the "dax" example above), RNNs fail spectacularly. We conclude with a proof-of-concept experiment in neural machine translation, supporting the conjecture that lack of systematicity is an important factor explaining why neural networks need very large training sets. | Under review STILL NOT SYSTEMATIC AFTER ALL THESE YEARS: ON THE COMPOSITIONAL SKILLS OF SEQUENCE-TO- SEQUENCE RECURRENT NETWORKS |
d235613622 | Differentiable Neural Architecture Search is one of the most popular Neural Architecture Search (NAS) methods for its search efficiency and simplicity, accomplished by jointly optimizing the model weight and architecture parameters in a weight-sharing supernet via gradient-based algorithms. At the end of the search phase, the operations with the largest architecture parameters will be selected to form the final architecture, with the implicit assumption that the values of architecture parameters reflect the operation strength. While much has been discussed about the supernet's optimization, the architecture selection process has received little attention. We provide empirical and theoretical analysis to show that the magnitude of architecture parameters does not necessarily indicate how much the operation contributes to the supernet's performance. We propose an alternative perturbation-based architecture selection that directly measures each operation's influence on the supernet. We re-evaluate several differentiable NAS methods with the proposed architecture selection and find that it is able to extract significantly improved architectures from the underlying supernets consistently. Furthermore, we find that several failure modes of DARTS can be greatly alleviated with the proposed selection method, indicating that much of the poor generalization observed in DARTS can be attributed to the failure of magnitude-based architecture selection rather than entirely the optimization of its supernet. 1 | Published as a conference paper at ICLR 2021 RETHINKING ARCHITECTURE SELECTION IN DIFFER- ENTIABLE NAS |
d44167055 | The problem of attributing a deep network's prediction to its input/base features is well-studied (cf.[1]). We introduce the notion of conductance to extend the notion of attribution to the understanding the importance of hidden units. Informally, the conductance of a hidden unit of a deep network is the flow of attribution via this hidden unit. We use conductance to understand the importance of a hidden unit to the prediction for a specific input, or over a set of inputs. We evaluate the effectiveness of conductance in multiple ways, including theoretical properties, ablation studies, and a feature selection task. The empirical evaluations are done using the Inception network over ImageNet data, and a sentiment analysis network over reviews. In both cases, we demonstrate the effectiveness of conductance in identifying interesting insights about the internal workings of these networks. | How Important Is a Neuron? |
d168169556 | Disentangled representations have recently been shown to improve fairness, data efficiency and generalisation in simple supervised and reinforcement learning tasks. To extend the benefits of disentangled representations to more complex domains and practical applications, it is important to enable hyperparameter tuning and model selection of existing unsupervised approaches without requiring access to ground truth attribute labels, which are not available for most datasets. This paper addresses this problem by introducing a simple yet robust and reliable method for unsupervised disentangled model selection. Our approach, Unsupervised Disentanglement Ranking (UDR) 1 , leverages the recent theoretical results that explain why variational autoencoders disentangle (Rolinek et al., 2019), to quantify the quality of disentanglement by performing pairwise comparisons between trained model representations. We show that our approach performs comparably to the existing supervised alternatives across 5400 models from six state of the art unsupervised disentangled representation learning model classes. Furthermore, we show that the ranking produced by our approach correlates well with the final task performance on two different domains. * Equal contribution. 1 We have released the code for our method as part of disentanglement_lib | Published as a conference paper at ICLR 2020 UNSUPERVISED MODEL SELECTION FOR VARIATIONAL DISENTANGLED REPRESENTATION LEARNING |
d1840346 | We present LR-GAN: an adversarial image generation model which takes scene structure and context into account. Unlike previous generative adversarial networks (GANs), the proposed GAN learns to generate image background and foregrounds separately and recursively, and stitch the foregrounds on the background in a contextually relevant manner to produce a complete natural image. For each foreground, the model learns to generate its appearance, shape and pose. The whole model is unsupervised, and is trained in an end-to-end manner with gradient descent methods. The experiments demonstrate that LR-GAN can generate more natural images with objects that are more human recognizable than DCGAN. The code is available at https://github.com/jwyang/lr-gan.pytorch. Learned-Miller. Labeled faces in the wild: A database for studying face recognition in unconstrained environments. | LR-GAN: LAYERED RECURSIVE GENERATIVE AD- VERSARIAL NETWORKS FOR IMAGE GENERATION |
d252519173 | Much recent research on information retrieval has focused on how to transfer from one task (typically with abundant supervised data) to various other tasks where supervision is limited, with the implicit assumption that it is possible to generalize from one task to all the rest. However, this overlooks the fact that there are many diverse and unique retrieval tasks, each targeting different search intents, queries, and search domains. In this paper, we suggest to work on Few-shot Dense Retrieval, a setting where each task comes with a short description and a few examples. To amplify the power of a few examples, we propose Promptbase Query Generation for Retriever (PROMPTAGATOR ), which leverages large language models (LLM) as a few-shot query generator, and creates task-specific retrievers based on the generated data. Powered by LLM's generalization ability, PROMPTAGATOR makes it possible to create task-specific end-to-end retrievers solely based on a few examples without using Natural Questions(Kwiatkowski et al., 2019)or MS MARCO (Nguyen et al., 2016) to train dual encoders. Surprisingly, LLM prompting with no more than 8 examples allows dual encoders to outperform heavily engineered models trained on MS MARCO like ColBERT v2(Santhanam et al., 2022)by more than 1.2 nDCG on average on 11 retrieval sets. Further training standard-size re-rankers using the same generated data yields another 5.0 point nDCG improvement. Our studies determine that query generation can be far more effective than previously observed, especially when a small amount of task-specific knowledge is given. | PROMPTAGATOR : FEW-SHOT DENSE RETRIEVAL FROM 8 EXAMPLES |
d13739955 | Many recently trained neural networks employ large numbers of parameters to achieve good performance. One may intuitively use the number of parameters required as a rough gauge of the difficulty of a problem. But how accurate are such notions? How many parameters are really needed? In this paper we attempt to answer this question by training networks not in their native parameter space, but instead in a smaller, randomly oriented subspace. We slowly increase the dimension of this subspace, note at which dimension solutions first appear, and define this to be the intrinsic dimension of the objective landscape. The approach is simple to implement, computationally tractable, and produces several suggestive conclusions. Many problems have smaller intrinsic dimensions than one might suspect, and the intrinsic dimension for a given dataset varies little across a family of models with vastly different sizes. This latter result has the profound implication that once a parameter space is large enough to solve a problem, extra parameters serve directly to increase the dimensionality of the solution manifold. Intrinsic dimension allows some quantitative comparison of problem difficulty across supervised, reinforcement, and other types of learning where we conclude, for example, that solving the inverted pendulum problem is 100 times easier than classifying digits from MNIST, and playing Atari Pong from pixels is about as hard as classifying CIFAR-10. In addition to providing new cartography of the objective landscapes wandered by parameterized models, the method is a simple technique for constructively obtaining an upper bound on the minimum description length of a solution. A byproduct of this construction is a simple approach for compressing networks, in some cases by more than 100 times. * Work performed as an intern at Uber AI Labs.Published as a conference paper at ICLR 2018 over these multi-dimensional surfaces. Therefore, any interpreted geography of these landscapes is valuable. | Published as a conference paper at ICLR 2018 MEASURING THE INTRINSIC DIMENSION OF OBJECTIVE LANDSCAPES |
d248665921 | A plethora of machine learning methods have been applied to imaging data, enabling the construction of clinically relevant imaging signatures of neurological and neuropsychiatric diseases. Oftentimes, such methods do not explicitly model the heterogeneity of disease effects, or approach it via nonlinear models that are not interpretable. Moreover, unsupervised methods may parse heterogeneity that is driven by nuisance confounding factors that affect global brain structure or function, rather than heterogeneity relevant to a pathology of interest. On the other hand, semi-supervised clustering methods seek to derive a pathology-related dichotomous subtypes, ignoring the truth that disease heterogeneity spatially and temporally extends along a continuum. To address the aforementioned limitations, herein, we propose a novel method, termed Surreal-GAN (Semi-SUpeRvised ReprEsentAtion Learning via GAN). Using cross-sectional imaging data, Surreal-GAN dissects underlying disease-related heterogeneity under the principle of semi-supervised clustering [cluster mappings from the normal control (CN) to patient (PT) domain], proposes a continuously dimensional representation, and infers the disease severity of patients at individual level along each dimension. The model first learns a transformation function from the CN domain to the PT domain with latent variables controlling transformation directions. An inverse mapping function together with regularization on function continuity, pattern orthogonality and monotonicity was also imposed to make sure that the transformation function captures necessarily meaningful imaging patterns with clinical significance. We first validated the model through semi-synthetic experiments, and then demonstrated its potential in capturing biologically plausible imaging patterns in Alzheimer's disease (AD). | SURREAL-GAN:SEMI-SUPERVISED REPRESENTATION LEARNING VIA GAN FOR UNCOVERING HETEROGE- NEOUS DISEASE-RELATED IMAGING PATTERNS |
d208139568 | We present a new method for black-box adversarial attack. Unlike previous methods that combined transfer-based and scored-based methods by using the gradient or initialization of a surrogate white-box model, this new method tries to learn a low-dimensional embedding using a pretrained model, and then performs efficient search within the embedding space to attack an unknown target network. The method produces adversarial perturbations with high level semantic patterns that are easily transferable. We show that this approach can greatly improve the query efficiency of black-box adversarial attack across different target network architectures. We evaluate our approach on MNIST, ImageNet and Google Cloud Vision API, resulting in a significant reduction on the number of queries. We also attack adversarially defended networks on CIFAR10 and ImageNet, where our method not only reduces the number of queries, but also improves the attack success rate. Keutzer. Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size. arXiv preprint arXiv:1602.07360, 2016.Andrew Ilyas, Logan Engstrom, Anish Athalye, and Jessy Lin. Black-box adversarial attacks with limited queries and information. | BLACK-BOX ADVERSARIAL ATTACK WITH TRANS- FERABLE MODEL-BASED EMBEDDING |
d219793033 | While second order optimizers such as natural gradient descent (NGD) often speed up optimization, their effect on generalization remains controversial. For instance, it has been pointed out that gradient descent (GD), in contrast to many preconditioned updates, converges to small Euclidean norm solutions in overparameterized models, leading to favorable generalization properties. This work presents a more nuanced view on the comparison of generalization between first-and second-order methods. We provide an exact asymptotic bias-variance decomposition of the generalization error of overparameterized ridgeless regression under a general class of preconditioner P , and consider the inverse population Fisher information matrix (used in NGD) as a particular example. We determine the optimal P for both the bias and variance, and find that the relative generalization performance of different optimizers depends on the label noise and the "shape" of the signal (true parameters): when the labels are noisy, the model is misspecified, or the signal is misaligned with the features, NGD can achieve lower risk; conversely, GD generalizes better than NGD under clean labels, a well-specified model, or aligned signal. Based on this analysis, we discuss several approaches to manage the bias-variance tradeoff, and the potential benefit of interpolating between GD and NGD. We then extend our analysis to regression in the reproducing kernel Hilbert space and demonstrate that preconditioned GD can decrease the population risk faster than GD. Lastly, we empirically compare the generalization performance of first-and second-order optimizers in neural network experiments, and observe robust trends matching our theoretical analysis.Setting P = I recovers ordinary gradient descent (GD). Choices of P which exploit second-order information include the inverse Fisher information matrix, which gives the natural gradient descent (NGD) [Ama98]; the inverse Hessian, which gives Newton's method [LBOM12]; and diagonal matrices estimated from past gradients, corresponding to various adaptive gradient methods [DHS11, KB14]. By using second-order information, these preconditioners often alleviate the effect of pathological curvature and speed up optimization. * Alphabetical ordering. Correspondence to: Denny Wu (dennywu@cs.toronto.edu).† RIKEN Center for Brain Science. amari@brain.riken.jp. | When Does Preconditioning Help or Hurt Generalization? * |
d57375742 | This work presents a new strategy for multi-class classification that requires no class-specific labels, but instead leverages pairwise similarity between examples, which is a weaker form of annotation. The proposed method, meta classification learning, optimizes a binary classifier for pairwise similarity prediction and through this process learns a multi-class classifier as a submodule. We formulate this approach, present a probabilistic graphical model for it, and derive a surprisingly simple loss function that can be used to learn neural network-based models. We then demonstrate that this same framework generalizes to the supervised, unsupervised cross-task, and semi-supervised settings. Our method is evaluated against state of the art in all three learning paradigms and shows a superior or comparable accuracy, providing evidence that learning multi-class classification without multi-class labels is a viable learning option. | MULTI-CLASS CLASSIFICATION WITHOUT MULTI- CLASS LABELS |
d210713989 | In this work, we address semi-supervised classification of graph data, where the categories of those unlabeled nodes are inferred from labeled nodes as well as graph structures. Recent works often solve this problem via advanced graph convolution in a conventionally supervised manner, but the performance could degrade significantly when labeled data is scarce. To this end, we propose a Graph Inference Learning (GIL) framework to boost the performance of semisupervised node classification by learning the inference of node labels on graph topology. To bridge the connection between two nodes, we formally define a structure relation by encapsulating node attributes, between-node paths, and local topological structures together, which can make the inference conveniently deduced from one node to another node. For learning the inference process, we further introduce meta-optimization on structure relations from training nodes to validation nodes, such that the learnt graph inference capability can be better self-adapted to testing nodes. Comprehensive evaluations on four benchmark datasets (including Cora, Citeseer, Pubmed, and NELL) demonstrate the superiority of our proposed GIL when compared against state-of-the-art methods on the semi-supervised node classification task. | Published as a conference paper at ICLR 2020 GRAPH INFERENCE LEARNING FOR SEMI-SUPERVISED CLASSIFICATION |
d247447155 | Recent studies on Learning to Optimize (L2O) suggest a promising path to automating and accelerating the optimization procedure for complicated tasks. Existing L2O models parameterize optimization rules by neural networks, and learn those numerical rules via meta-training. However, they face two common pitfalls: (1) scalability: the numerical rules represented by neural networks create extra memory overhead for applying L2O models, and limit their applicability to optimizing larger tasks; (2) interpretability: it is unclear what an L2O model has learned in its black-box optimization rule, nor is it straightforward to compare different L2O models in an explainable way. To avoid both pitfalls, this paper proves the concept that we can "kill two birds by one stone", by introducing the powerful tool of symbolic regression to L2O. In this paper, we establish a holistic symbolic representation and analysis framework for L2O, which yields a series of insights for learnable optimizers. Leveraging our findings, we further propose a lightweight L2O model that can be meta-trained on large-scale problems and outperformed human-designed and tuned optimizers. Our work is set to supply a brand-new perspective to L2O research. Codes are available at: https: //github.com/VITA-Group/Symbolic-Learning-To-Optimize.arXiv:2203.06578v4 [cs.LG] 7 May 2022Published as a conference paper at ICLR 2022 to explain, analyze, troubleshoot, and prove. In contrast, existing L2O approaches fit update models using sophisticated data-driven predictors such as neural networks. As a result, it is unclear what rules have been learned in those "black boxes", prohibiting their further explainable analysis and comparison. W Zheng, Anum Ali, N González-Prelcic, RW Heath, Aldebaro Klautau, and E Moradi Pari. 5g v2x communication at millimeter wave: Rate maps and use cases. In 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), pp. 1-5. IEEE, 2020.Wenqing Zheng and Nuria González-Prelcic. Joint position, orientation and channel estimation in hybrid mmwave mimo systems.bian. Cold brew: Distilling graph node representations with incomplete or missing neighborhoods. arXiv preprint arXiv:2111.04840, 2021b. | Published as a conference paper at ICLR 2022 SYMBOLIC LEARNING TO OPTIMIZE: TOWARDS IN- TERPRETABILITY AND SCALABILITY |
d9398766 | We propose local distributional smoothness (LDS), a new notion of smoothness for statistical model that can be used as a regularization term to promote the smoothness of the model distribution. We named the LDS based regularization as virtual adversarial training (VAT). The LDS of a model at an input datapoint is defined as the KL-divergence based robustness of the model distribution against local perturbation around the datapoint. VAT resembles adversarial training, but distinguishes itself in that it determines the adversarial direction from the model distribution alone without using the label information, making it applicable to semi-supervised learning. The computational cost for VAT is relatively low. For neural network, the approximated gradient of the LDS can be computed with no more than three pairs of forward and back propagations. When we applied our technique to supervised and semi-supervised learning for the MNIST dataset, it outperformed all the training methods other than the current state of the art method, which is based on a highly advanced generative model. We also applied our method to SVHN and NORB, and confirmed our method's superior performance over the current state of the art semi-supervised method applied to these datasets. | DISTRIBUTIONAL SMOOTHING WITH VIRTUAL ADVERSARIAL TRAINING |
d203593798 | We study non-collaborative dialogs, where two agents have a conflict of interest but must strategically communicate to reach an agreement (e.g., negotiation). This setting poses new challenges for modeling dialog history because the dialog's outcome relies not only on the semantic intent, but also on tactics that convey the intent. We propose to model both semantic and tactic history using finite state transducers (FSTs). Unlike RNN, FSTs can explicitly represent dialog history through all the states traversed, facilitating interpretability of dialog structure. We train FSTs on a set of strategies and tactics used in negotiation dialogs. The trained FSTs show plausible tactic structure and can be generalized to other noncollaborative domains (e.g., persuasion). We evaluate the FSTs by incorporating them in an automated negotiating system that attempts to sell products and a persuasion system that persuades people to donate to a charity. Experiments show that explicitly modeling both semantic and tactic history is an effective way to improve both dialog policy planning and generation performance. | AUGMENTING NON-COLLABORATIVE DIALOG SYS- TEMS WITH EXPLICIT SEMANTIC AND STRATEGIC DI- ALOG HISTORY |
d239016251 | Learning from set-structured data is a fundamental problem that has recently attracted increasing attention, where a series of summary networks are introduced to deal with the set input. In fact, many meta-learning problems can be treated as set-input tasks. Most existing summary networks aim to design different architectures for the input set in order to enforce permutation invariance. However, scant attention has been paid to the common cases where different sets in a metadistribution are closely related and share certain statistical properties. Viewing each set as a distribution over a set of global prototypes, this paper provides a novel prototype-oriented optimal transport (POT) framework to improve existing summary networks. To learn the distribution over the global prototypes, we minimize its regularized optimal transport distance to the set empirical distribution over data points, providing a natural unsupervised way to improve the summary network. Since our plug-and-play framework can be applied to many metalearning problems, we further instantiate it to the cases of few-shot classification and implicit meta generative modeling. Extensive experiments demonstrate that our framework significantly improves the existing summary networks on learning more powerful summary statistics from sets and can be successfully integrated into metric-based few-shot classification and generative modeling applications, providing a promising tool for addressing set-input and meta-learning problems. | LEARNING PROTOTYPE-ORIENTED SET REPRESENTA- TIONS FOR META-LEARNING |
d255416037 | The approximate nearest neighbor (ANN) search problem is fundamental to efficiently serving many real-world machine learning applications. A number of techniques have been developed for ANN search that are efficient, accurate, and scalable. However, such techniques typically have a number of parameters that affect the speed-recall tradeoff, and exhibit poor performance when such parameters aren't properly set. Tuning these parameters has traditionally been a manual process, demanding in-depth knowledge of the underlying search algorithm. This is becoming an increasingly unrealistic demand as ANN search grows in popularity. To tackle this obstacle to ANN adoption, this work proposes a constrained optimization-based approach to tuning quantization-based ANN algorithms. Our technique takes just a desired search cost or recall as input, and then generates tunings that, empirically, are very close to the speed-recall Pareto frontier and give leading performance on standard benchmarks. Table 1: Our technique is the first to use minimal computational cost and human involvement to configure an ANN index to perform very close to its speed-recall Pareto frontier. Method Computational Cost of Tuning Human Involvement Hyperparameter Quality Grid search High Low High Manual tuning Low High Medium Black-box optimizer Medium Low Medium Ours Low Low High during the tuning process, necessitating extensive human-in-the-loop expertise, or giving suboptimal hyperparameters.Mitigating these issues is becoming increasingly important with the growth in dataset sizes and in the popularity of the ANN-based retrieval paradigm. This paper describes how highly performant ANN indices may be created and tuned with minimal configuration complexity to the end user. Our contributions are:• Deriving theoretically-grounded models for recall and search cost for quantization-based ANN algorithms, and presenting an efficient Lagrange multipliers-based technique for optimizing either of these metrics with respect to the other. | AUTOMATING NEAREST NEIGHBOR SEARCH CONFIG- URATION WITH CONSTRAINED OPTIMIZATION |
d237416585 | This paper explores a simple method for improving the zero-shot learning abilities of language models. We show that instruction tuning-finetuning language models on a collection of datasets described via instructions-substantially improves zeroshot performance on unseen tasks. | Published as a conference paper at ICLR 2022 FINETUNED LANGUAGE MODELS ARE ZERO-SHOT LEARNERS |
d222133140 | In adversarial machine learning, there was a common belief that robustness and accuracy hurt each other. The belief was challenged by recent studies where we can maintain the robustness and improve the accuracy. However, the other direction, we can keep the accuracy and improve the robustness, is conceptually and practically more interesting, since robust accuracy should be lower than standard accuracy for any model. In this paper, we show this direction is also promising. Firstly, we find even over-parameterized deep networks may still have insufficient model capacity, because adversarial training has an overwhelming smoothing effect. Secondly, given limited model capacity, we argue adversarial data should have unequal importance: geometrically speaking, a natural data point closer to/farther from the class boundary is less/more robust, and the corresponding adversarial data point should be assigned with larger/smaller weight. Finally, to implement the idea, we propose geometry-aware instance-reweighted adversarial training, where the weights are based on how difficult it is to attack a natural data point. Experiments show that our proposal boosts the robustness of standard adversarial training; combining two directions, we improve both robustness and accuracy of standard adversarial training. training: Direct input space margin maximization through adversarial training. In ICLR, 2020. Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard. The robustness of deep networks: A geometrical perspective. IEEE Signal Processing Magazine, 34(6):50-62, 2017. Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, and Stefano Soatto. Empirical study of the topology and geometry of deep networks. In CVPR, 2018.Yoav Freund and Robert E Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. | Published as a conference paper at ICLR 2021 GEOMETRY-AWARE INSTANCE-REWEIGHTED ADVER- SARIAL TRAINING |
d256598148 | This paper presents a novel end-to-end framework with Explicit box Detection for multi-person Pose estimation, called ED-Pose, where it unifies the contextual learning between human-level (global) and keypoint-level (local) information. Different from previous one-stage methods, ED-Pose re-considers this task as two explicit box detection processes with a unified representation and regression supervision. First, we introduce a human detection decoder from encoded tokens to extract global features. It can provide a good initialization for the latter keypoint detection, making the training process converge fast. Second, to bring in contextual information near keypoints, we regard pose estimation as a keypoint box detection problem to learn both box positions and contents for each keypoint. A human-to-keypoint detection decoder adopts an interactive learning strategy between human and keypoint features to further enhance global and local feature aggregation. In general, ED-Pose is conceptually simple without postprocessing and dense heatmap supervision. It demonstrates its effectiveness and efficiency compared with both two-stage and one-stage methods. Notably, explicit box detection boosts the pose estimation performance by 4.5 AP on COCO and 9.9 AP on CrowdPose. For the first time, as a fully end-to-end framework with a L1 regression loss, ED-Pose surpasses heatmap-based Top-down methods under the same backbone by 1.2 AP on COCO and achieves the state-of-theart with 76.6 AP on CrowdPose without bells and whistles. | Published as a conference paper at ICLR 2023 EXPLICIT BOX DETECTION UNIFIES END-TO-END MULTI-PERSON POSE ESTIMATION |
d231786358 | Wasserstein barycenters provide a geometric notion of the weighted average of probability measures based on optimal transport. In this paper, we present a scalable algorithm to compute Wasserstein-2 barycenters given sample access to the input measures, which are not restricted to being discrete. While past approaches rely on entropic or quadratic regularization, we employ input convex neural networks and cycle-consistency regularization to avoid introducing bias. As a result, our approach does not resort to minimax optimization. We provide theoretical analysis on error bounds as well as empirical evidence of the effectiveness of the proposed approach in low-dimensional qualitative scenarios and high-dimensional quantitative experiments. arXiv:2102.01752v1 [cs.LG] 2 Feb 2021 arXiv pre-print Wasserstein-2 distances, the gradients of the resulting convex potentials "push" the input distributions close to the true barycenter, allowing good approximation of the barycenter. | arXiv pre-print CONTINUOUS WASSERSTEIN-2 BARYCENTER ESTIMATION WITHOUT MINIMAX OPTIMIZATION |
d61153651 | AWe study the interplay between memorization and generalization of overparameterized networks in the extreme case of a single training example and an identity-mapping task. We examine fully-connected and convolutional networks (FCN and CNN), both linear and nonlinear, initialized randomly and then trained to minimize the reconstruction error. The trained networks stereotypically take one of two forms: the constant function (memorization) and the identity function (generalization). We formally characterize generalization in single-layer FCNs and CNNs. We show empirically that di erent architectures exhibit strikingly di erent inductive biases. For example, CNNs of up to 10 layers are able to generalize from a single example, whereas FCNs cannot learn the identity function reliably from 60k examples. Deeper CNNs often fail, but nonetheless do astonishing work to memorize the training output: because CNN biases are location invariant, the model must progressively grow an output pattern from the image boundaries via the coordination of many layers. Our work helps to quantify and visualize the sensitivity of inductive biases to architectural choices such as depth, kernel width, and number of channels. | Published as a conference paper at ICLR 2020 I C : M G E O |
d257364778 | Open-ended learning methods that automatically generate a curriculum of increasingly challenging tasks serve as a promising avenue toward generally capable reinforcement learning agents. Existing methods adapt curricula independently over either environment parameters (in single-agent settings) or co-player policies (in multi-agent settings). However, the strengths and weaknesses of co-players can manifest themselves differently depending on environmental features. It is thus crucial to consider the dependency between the environment and co-player when shaping a curriculum in multi-agent domains. In this work, we use this insight and extend Unsupervised Environment Design (UED) to multi-agent environments. We then introduce Multi-Agent Environment Design Strategist for Open-Ended Learning (MAESTRO), the first multi-agent UED approach for two-player zero-sum settings. MAESTRO efficiently produces adversarial, joint curricula over both environments and co-players and attains minimax-regret guarantees at Nash equilibrium. Our experiments show that MAESTRO outperforms a number of strong baselines on competitive two-player games, spanning discrete and continuous control settings. 1 | Published as a conference paper at ICLR 2023 MAESTRO: OPEN-ENDED ENVIRONMENT DESIGN FOR MULTI-AGENT REINFORCEMENT LEARNING |
d238420480 | The importance of Variational Autoencoders reaches far beyond standalone generative models -the approach is also used for learning latent representations and can be generalized to semi-supervised learning.This requires a thorough analysis of their commonly known shortcomings: posterior collapse and approximation errors.This paper analyzes VAE approximation errors caused by the combination of the ELBO objective and encoder models from conditional exponential families, including, but not limited to, commonly used conditionally independent discrete and continuous models.We characterize subclasses of generative models consistent with these encoder families.We show that the ELBO optimizer is pulled away from the likelihood optimizer towards the consistent subset and study this effect experimentally.Importantly, this subset can not be enlarged, and the respective error cannot be decreased, by considering deeper encoder/decoder networks. | |
d207847275 | Non-autoregressive machine translation (NAT) systems predict a sequence of output tokens in parallel, achieving substantial improvements in generation speed compared to autoregressive models. Existing NAT models usually rely on the technique of knowledge distillation, which creates the training data from a pretrained autoregressive model for better performance. Knowledge distillation is empirically useful, leading to large gains in accuracy for NAT models, but the reason for this success has, as of yet, been unclear. In this paper, we first design systematic experiments to investigate why knowledge distillation is crucial to NAT training. We find that knowledge distillation can reduce the complexity of data sets and help NAT to model the variations in the output data. Furthermore, a strong correlation is observed between the capacity of an NAT model and the optimal complexity of the distilled data for the best translation quality. Based on these findings, we further propose several approaches that can alter the complexity of data sets to improve the performance of NAT models. We achieve the state-of-the-art performance for the NAT-based models, and close the gap with the autoregressive baseline on WMT14 En-De benchmark. 1Preprint. Under review• What is the relationship between the NAT model (student) and the AT model (teacher)? Are different varieties of distilled data better for different NAT models?• Due to distillation, the performance of NAT models is largely bounded by the choice of AT teacher. Is there a way to further close the performance gap with standard AT models?In this paper, we aim to answer the three questions above, improving understanding of knowledge distillation through empirical analysis over a variety of AT and NAT models. Specifically, our contributions are as follows:• We first visualize explicitly on a synthetic dataset how modes are reduced by distillation ( §3.1). Inspired by the synthetic experiments, we further propose metrics for measuring complexity and faithfulness for a given training set. Specifically, our metrics are the conditional entropy and KL-divergence of word translation based on an external alignment tool, and we show these are correlated with NAT model performance ( §3.2).• We conduct a systematic analysis ( §4) over four AT teacher models and six NAT student models with various architectures on the standard WMT14 English-German translation benchmark. These experiments find a strong correlation between the capacity of an NAT model and the optimal dataset complexity for the best translation quality. | Preprint. Under review UNDERSTANDING KNOWLEDGE DISTILLATION IN NON-AUTOREGRESSIVE MACHINE TRANSLATION |
d80628419 | Despite impressive performance as evaluated on i.i.d. holdout data, deep neural networks depend heavily on superficial statistics of the training data and are liable to break under distribution shift. For example, subtle changes to the background or texture of an image can break a seemingly powerful classifier. Building on previous work on domain generalization, we hope to produce a classifier that will generalize to previously unseen domains, even when domain identifiers are not available during training. This setting is challenging because the model may extract many distribution-specific (superficial) signals together with distribution-agnostic (semantic) signals. To overcome this challenge, we incorporate the gray-level cooccurrence matrix (GLCM) to extract patterns that our prior knowledge suggests are superficial: they are sensitive to texture but unable to capture the gestalt of an image. Then we introduce two techniques for improving our networks' outof-sample performance. The first method is built on the reverse gradient method that pushes our model to learn representations from which the GLCM representation is not predictable. The second method is built on the independence introduced by projecting the model's representation onto the subspace orthogonal to GLCM representation's. We test our method on battery of standard domain generalization data sets and, interestingly, achieve comparable or better performance as compared to other domain generalization methods that explicitly require samples from the target distribution for training. | LEARNING ROBUST REPRESENTATIONS BY PROJECT- ING SUPERFICIAL STATISTICS OUT |
d256627643 | Deep neural networks (DNNs) are vulnerable to backdoor attacks, where adversaries embed a hidden backdoor trigger during the training process for malicious prediction manipulation. These attacks pose great threats to the applications of DNNs under the real-world machine learning as a service (MLaaS) setting, where the deployed model is fully black-box while the users can only query and obtain its predictions. Currently, there are many existing defenses to reduce backdoor threats. However, almost all of them cannot be adopted in MLaaS scenarios since they require getting access to or even modifying the suspicious models. In this paper, we propose a simple yet effective black-box input-level backdoor detection, called SCALE-UP, which requires only the predicted labels to alleviate this problem. Specifically, we identify and filter malicious testing samples by analyzing their prediction consistency during the pixel-wise amplification process. Our defense is motivated by an intriguing observation (dubbed scaled prediction consistency) that the predictions of poisoned samples are significantly more consistent compared to those of benign ones when amplifying all pixel values. Besides, we also provide theoretical foundations to explain this phenomenon. Extensive experiments are conducted on benchmark datasets, verifying the effectiveness and efficiency of our defense and its resistance to potential adaptive attacks. Our codes are available at https://github.com/JunfengGo/SCALE-UP. | SCALE-UP: AN EFFICIENT BLACK-BOX INPUT- LEVEL BACKDOOR DETECTION VIA ANALYZING SCALED PREDICTION CONSISTENCY |
d195750622 | Data selection methods such as active learning and core-set selection are useful tools for machine learning on large datasets, but they can be prohibitively expensive to apply in deep learning. Unlike in other areas of machine learning, the feature representations that these techniques depend on are learned in deep learning rather than given, which takes a substantial amount of training time. In this work, we show that we can significantly improve the computational efficiency of data selection in deep learning by using a much smaller proxy model to perform data selection for tasks that will eventually require a large target model (e.g., selecting data points to label for active learning). In deep learning, we can scale down models by removing hidden layers or reducing their dimension to create proxies that are an order of magnitude faster. Although these small proxy models have significantly higher error, we find that they empirically provide useful rankings for data selection that have a high correlation with those of larger models. We evaluate this "selection via proxy" (SVP) approach on several data selection tasks. For active learning, applying SVP to Sener and Savarese [2018]'s recent method for active learning in deep learning gives a 4× improvement in execution time while yielding the same model accuracy. For core-set selection, we show that a proxy model that trains 10× faster than a target ResNet164 model on CIFAR10 can be used to remove 50% of the training data without compromising the accuracy of the target model, making end-to-end training time improvements via core-set selection possible. * updated work from Coleman et al. [2019] Preprint. Under review. | Selection Via Proxy: Efficient Data Selection For Deep Learning * |
d256808572 | This paper proposes a novel batch normalization strategy for test-time adaptation. Recent test-time adaptation methods heavily rely on the modified batch normalization, i.e., transductive batch normalization (TBN), which calculates the mean and the variance from the current test batch rather than using the running mean and variance obtained from source data, i.e., conventional batch normalization (CBN). Adopting TBN that employs test batch statistics mitigates the performance degradation caused by the domain shift. However, re-estimating normalization statistics using test data depends on impractical assumptions that a test batch should be large enough and be drawn from i.i.d. stream, and we observed that the previous methods with TBN show critical performance drop without the assumptions. In this paper, we identify that CBN and TBN are in a trade-off relationship and present a new test-time normalization (TTN) method that interpolates the standardization statistics by adjusting the importance between CBN and TBN according to the domain-shift sensitivity of each BN layer. Our proposed TTN improves model robustness to shifted domains across a wide range of batch sizes and in various realistic evaluation scenarios. TTN is widely applicable to other test-time adaptation methods that rely on updating model parameters via backpropagation. We demonstrate that adopting TTN further improves their performance and achieves state-of-the-art performance in various standard benchmarks. | Published as a conference paper at ICLR 2023 TTN: A DOMAIN-SHIFT AWARE BATCH NORMALIZA- TION IN TEST-TIME ADAPTATION |
d17707860 | An intriguing property of deep neural networks is the existence of adversarial examples, which can transfer among different architectures. These transferable adversarial examples may severely hinder deep neural network-based applications. Previous works mostly study the transferability using small scale datasets. In this work, we are the first to conduct an extensive study of the transferability over large models and a large scale dataset, and we are also the first to study the transferability of targeted adversarial examples with their target labels. We study both non-targeted and targeted adversarial examples, and show that while transferable non-targeted adversarial examples are easy to find, targeted adversarial examples generated using existing approaches almost never transfer with their target labels. Therefore, we propose novel ensemble-based approaches to generating transferable adversarial examples. Using such approaches, we observe a large proportion of targeted adversarial examples that are able to transfer with their target labels for the first time. We also present some geometric studies to help understanding the transferable adversarial examples. Finally, we show that the adversarial examples generated using ensemble-based approaches can successfully attack Clarifai.com, | Published as a conference paper at ICLR 2017 DELVING INTO TRANSFERABLE ADVERSARIAL EX- AMPLES AND BLACK-BOX ATTACKS |
d258048386 | We present a method to map 2D image observations of a scene to a persistent 3D scene representation, enabling novel view synthesis and disentangled representation of the movable and immovable components of the scene. Motivated by the bird's-eye-view (BEV) representation commonly used in vision and robotics, we propose conditional neural groundplans, ground-aligned 2D feature grids, as persistent and memory-efficient scene representations. Our method is trained selfsupervised from unlabeled multi-view observations using differentiable rendering, and learns to complete geometry and appearance of occluded regions. In addition, we show that we can leverage multi-view videos at training time to learn to separately reconstruct static and movable components of the scene from a single image at test time. The ability to separately reconstruct movable objects enables a variety of downstream tasks using simple heuristics, such as extraction of objectcentric 3D representations, novel view synthesis, instance-level segmentation, 3D bounding box prediction, and scene editing. This highlights the value of neural groundplans as a backbone for efficient 3D scene understanding models. * | Published as a conference paper at ICLR 2023 NEURAL GROUNDPLANS: PERSISTENT NEURAL SCENE REPRESENTATIONS FROM A SINGLE IMAGE |
d200884 | Many machine learning systems are built to solve the hardest examples of a particular task, which often makes them large and expensive to run-especially with respect to the easier examples, which might require much less computation. For an agent with a limited computational budget, this "one-size-fits-all" approach may result in the agent wasting valuable computation on easy examples, while not spending enough on hard examples. Rather than learning a single, fixed policy for solving all instances of a task, we introduce a metacontroller which learns to optimize a sequence of "imagined" internal simulations over predictive models of the world in order to construct a more informed, and more economical, solution. The metacontroller component is a model-free reinforcement learning agent, which decides both how many iterations of the optimization procedure to run, as well as which model to consult on each iteration. The models (which we call "experts") can be state transition models, action-value functions, or any other mechanism that provides information useful for solving the task, and can be learned on-policy or off-policy in parallel with the metacontroller. When the metacontroller, controller, and experts were trained with "interaction networks" (Battaglia et al., 2016) as expert models, our approach was able to solve a challenging decision-making problem under complex non-linear dynamics. The metacontroller learned to adapt the amount of computation it performed to the difficulty of the task, and learned how to choose which experts to consult by factoring in both their reliability and individual computational resource costs. This allowed the metacontroller to achieve a lower overall cost (task loss plus computational cost) than more traditional fixed policy approaches. These results demonstrate that our approach is a powerful framework for using rich forward models for efficient model-based reinforcement learning. | Published as a conference paper at ICLR 2017 METACONTROL FOR ADAPTIVE IMAGINATION-BASED OPTIMIZATION |
d253734705 | Electron cryo-microscopy (cryo-EM) produces three-dimensional (3D) maps of the electrostatic potential of biological macromolecules, including proteins. Along with knowledge about the imaged molecules, cryo-EM maps allow de novo atomic modeling, which is typically done through a laborious manual process. Taking inspiration from recent advances in machine learning applications to protein structure prediction, we propose a graph neural network (GNN) approach for the automated model building of proteins in cryo-EM maps. The GNN acts on a graph with nodes assigned to individual amino acids and edges representing the protein chain. Combining information from the voxel-based cryo-EM data, the amino acid sequence data, and prior knowledge about protein geometries, the GNN refines the geometry of the protein chain and classifies the amino acids for each of its nodes. Application to 28 test cases shows that our approach outperforms the state-of-the-art and approximates manual building for cryo-EM maps with resolutions better than 3.5Å 1 . | A GRAPH NEURAL NETWORK APPROACH TO AUTO- MATED MODEL BUILDING IN CRYO-EM MAPS |
d249097738 | Nonlinear dynamics is ubiquitous in nature and commonly seen in various science and engineering disciplines. Distilling analytical expressions that govern nonlinear dynamics from limited data remains vital but challenging. To tackle this fundamental issue, we propose a novel Symbolic Physics Learner (SPL) machine to discover the mathematical structure of nonlinear dynamics. The key concept is to interpret mathematical operations and system state variables by computational rules and symbols, establish symbolic reasoning of mathematical formulas via expression trees, and employ a Monte Carlo tree search (MCTS) agent to explore optimal expression trees based on measurement data. The MCTS agent obtains an optimistic selection policy through the traversal of expression trees, featuring the one that maps to the arithmetic expression of underlying physics. Salient features of the proposed framework include search flexibility and enforcement of parsimony for discovered equations. The efficacy and superiority of the SPL machine are demonstrated by numerical examples, compared with state-of-the-art baselines.Published as a conference paper at ICLR 2023 sparsity-promoting approach relies on a properly defined candidate function library that requires good prior knowledge of the system. It is also restricted by the fact that a linear combination of candidate functions might be insufficient to recover complicated mathematical expressions. Moreover, when the library size is massive, it empirically fails to hold the sparsity constraint. | Published as a conference paper at ICLR 2023 SYMBOLIC PHYSICS LEARNER: DISCOVERING GOV- ERNING EQUATIONS VIA MONTE CARLO TREE SEARCH |
d249191700 | Quantile regression (QR) is a powerful tool for estimating one or more conditional quantiles of a target variable Y given explanatory features X. A limitation of QR is that it is only defined for scalar target variables, due to the formulation of its objective function, and since the notion of quantiles has no standard definition for multivariate distributions. Recently, vector quantile regression (VQR) was proposed as an extension of QR for vector-valued target variables, thanks to a meaningful generalization of the notion of quantiles to multivariate distributions via optimal transport. Despite its elegance, VQR is arguably not applicable in practice due to several limitations: (i) it assumes a linear model for the quantiles of the target Y given the features X; (ii) its exact formulation is intractable even for modestly-sized problems in terms of target dimensions, number of regressed quantile levels, or number of features, and its relaxed dual formulation may violate the monotonicity of the estimated quantiles; (iii) no fast or scalable solvers for VQR currently exist. In this work we fully address these limitations, namely: (i) We extend VQR to the non-linear case, showing substantial improvement over linear VQR; (ii) We propose vector monotone rearrangement, a method which ensures the quantile functions estimated by VQR are monotone functions; (iii) We provide fast, GPU-accelerated solvers for linear and nonlinear VQR which maintain a fixed memory footprint, and demonstrate that they scale to millions of samples and thousands of quantile levels; (iv) We release an optimized python package of our solvers as to widespread the use of VQR in real-world applications.arXiv:2205.14977v3 [stat.CO] 2 Jun 2023Scalable VQR. We introduce a highly-scalable solver for VQR in Section 3. Our approach, inspired byGenevay et al. (2016) andCarlier et al. (2020), relies on solving a new relaxed dual formulation of the VQR problem. We propose custom stochastic-gradient-based solvers which maintain a constant memory footprint regardless of problem size. We demonstrate that our approach scales to millions of samples and thousands of quantile levels and allows for GPU-acceleration.Nonlinear VQR. To address the limitation of linear specification, in Section 4 we propose nonlinear vector quantile regression (NL-VQR). The key idea is fit a nonlinear embedding function of the input features jointly with the regression coefficients. This is made possible by leveraging the relaxed dual formulation and solver introduced in Section 3. We demonstrate, through synthetic and real-data experiments, that nonlinear VQR can model complex conditional quantile functions substantially better than linear VQR and separable QR approaches.Vector monotone rearrangement (VMR). In Section 5 we propose VMR, which resolves the co-monotonicity violations in estimated CVQFs. We solve an optimal transport problem to rearrange . Calibrated multiple-output quantile regression with representation learning. arXiv preprint arXiv:2110.00816, 2021. | Published as a conference paper at ICLR 2023 FAST NONLINEAR VECTOR QUANTILE REGRESSION |
d159041190 | Transfer learning, in which a network is trained on one task and re-purposed on another, is often used to produce neural network classifiers when data is scarce or full-scale training is too costly. When the goal is to produce a model that is not only accurate but also adversarially robust, data scarcity and computational limitations become even more cumbersome. We consider robust transfer learning, in which we transfer not only performance but also robustness from a source model to a target domain. We start by observing that robust networks contain robust feature extractors. By training classifiers on top of these feature extractors, we produce new models that inherit the robustness of their parent networks. We then consider the case of "fine tuning" a network by re-training end-to-end in the target domain. When using lifelong learning strategies, this process preserves the robustness of the source network while achieving high accuracy. By using such strategies, it is possible to produce accurate and robust models with little data, and without the cost of adversarial training. Additionally, we can improve the generalization of adversarially trained models, while maintaining their robustness. * equal contribution | Published as a conference paper at ICLR 2020 ADVERSARIALLY ROBUST TRANSFER LEARNING |
d14715110 | Recurrent neural network is a powerful model that learns temporal patterns in sequential data. For a long time, it was believed that recurrent networks are difficult to train using simple optimizers, such as stochastic gradient descent, due to the so-called vanishing gradient problem. In this paper, we show that learning longer term patterns in real data, such as in natural language, is perfectly possible using gradient descent. This is achieved by using a slight structural modification of the simple recurrent neural network architecture. We encourage some of the hidden units to change their state slowly by making part of the recurrent weight matrix close to identity, thus forming a kind of longer term memory. We evaluate our model on language modeling tasks on benchmark datasets, where we obtain similar performance to the much more complex Long Short Term Memory (LSTM) networks(Hochreiter & Schmidhuber, 1997). | LEARNING LONGER MEMORY IN RECURRENT NEURAL NETWORKS |
d5273326 | We introduce the "exponential linear unit" (ELU) which speeds up learning in deep neural networks and leads to higher classification accuracies. Like rectified linear units (ReLUs), leaky ReLUs (LReLUs) and parametrized ReLUs (PRe-LUs), ELUs alleviate the vanishing gradient problem via the identity for positive values. However ELUs have improved learning characteristics compared to the units with other activation functions. In contrast to ReLUs, ELUs have negative values which allows them to push mean unit activations closer to zero like batch normalization but with lower computational complexity. Mean shifts toward zero speed up learning by bringing the normal gradient closer to the unit natural gradient because of a reduced bias shift effect. While LReLUs and PReLUs have negative values, too, they do not ensure a noise-robust deactivation state. ELUs saturate to a negative value with smaller inputs and thereby decrease the forward propagated variation and information. Therefore ELUs code the degree of presence of particular phenomena in the input, while they do not quantitatively model the degree of their absence. In experiments, ELUs lead not only to faster learning, but also to significantly better generalization performance than ReLUs and LReLUs on networks with more than 5 layers. On CIFAR-100 ELUs networks significantly outperform ReLU networks with batch normalization while batch normalization does not improve ELU networks. ELU networks are among the top 10 reported CIFAR-10 results and yield the best published result on CIFAR-100, without resorting to multi-view evaluation or model averaging. On ImageNet, ELU networks considerably speed up learning compared to a ReLU network with the same architecture, obtaining less than 10% classification error for a single crop, single model network. | Published as a conference paper at ICLR 2016 FAST AND ACCURATE DEEP NETWORK LEARNING BY EXPONENTIAL LINEAR UNITS (ELUS) |
d14426518 | We introduce a simple new regularizer for auto-encoders whose hidden-unit activation functions contain at least one zero-gradient (saturated) region. This regularizer explicitly encourages activations in the saturated region(s) of the corresponding activation function. We call these Saturating Auto-Encoders (SATAE). We show that the saturation regularizer explicitly limits the SATAE's ability to reconstruct inputs which are not near the data manifold. Furthermore, we show that a wide variety of features can be learned when different activation functions are used. Finally, connections are established with the Contractive and Sparse Auto-Encoders. * The authors thank Joan Bruna and David Eigen for their useful suggestions and comments. | Saturating Auto-Encoders |
d10052258 | PLAYING SNES IN THE RETRO LEARNING ENVIRONMENT | |
d252715683 | Optimizing multiple competing objectives is a common problem across science and industry.The inherent inextricable trade-off between those objectives leads one to the task of exploring their Pareto front.A meaningful quantity for the purpose of the latter is the hypervolume indicator, which is used in Bayesian Optimization (BO) and Evolutionary Algorithms (EAs).However, the computational complexity for the calculation of the hypervolume scales unfavorably with an increasing number of objectives and data points, which restricts its use in those common multiobjective optimization frameworks.To overcome these restrictions, previous work has focused on approximating the hypervolume using deep learning.In this work, we propose a novel deep learning architecture to approximate the hypervolume function, which we call DeepHV.For better sample efficiency and generalization, we exploit the fact that the hypervolume is scale equivariant in each of the objectives as well as permutation invariant w.r.t.both the objectives and the samples, by using a deep neural network that is equivariant w.r.t. the combined group of scalings and permutations.We show through an ablation study that including these symmetries leads to significantly improved model accuracy.We evaluate our method against exact, and approximate hypervolume methods in terms of accuracy, computation time, and generalization.We also apply and compare our methods to state-of-theart multi-objective BO methods and EAs on a range of synthetic and real-world benchmark test cases.The results show that our methods are promising for such multi-objective optimization tasks. | |
d231592887 | The two main impediments to continual learning are catastrophic forgetting and memory limitations on the storage of data. To cope with these challenges, we propose a novel, cognitively-inspired approach which trains autoencoders with Neural Style Transfer to encode and store images. During training on a new task, reconstructed images from encoded episodes are replayed in order to avoid catastrophic forgetting. The loss function for the reconstructed images is weighted to reduce its effect during classifier training to cope with image degradation. When the system runs out of memory the encoded episodes are converted into centroids and covariance matrices, which are used to generate pseudo-images during classifier training, keeping classifier performance stable while using less memory. Our approach increases classification accuracy by 13-17% over state-of-the-art methods on benchmark datasets, while requiring 78% less storage space. 1 | EEC: LEARNING TO ENCODE AND REGENERATE IM- AGES FOR CONTINUAL LEARNING |
d256868505 | Quasar convexity is a condition that allows some first-order methods to efficiently minimize a function even when the optimization landscape is non-convex. Previous works develop near-optimal accelerated algorithms for minimizing this class of functions, however, they require a subroutine of binary search which results in multiple calls to gradient evaluations in each iteration, and consequently the total number of gradient evaluations does not match a known lower bound. In this work, we show that a recently proposed continuized Nesterov acceleration can be applied to minimizing quasar convex functions and achieves the optimal bound with a high probability. Furthermore, we find that the objective functions of training generalized linear models (GLMs) satisfy quasar convexity, which broadens the applicability of the relevant algorithms, while known practical examples of quasar convexity in non-convex learning are sparse in the literature. We also show that if a smooth and one-point strongly convex, Polyak-Łojasiewicz, or quadratic-growth function satisfies quasar convexity, then attaining an accelerated linear rate for minimizing the function is possible under certain conditions, while acceleration is not known in general for these classes of functions. | Published as a conference paper at ICLR 2023 CONTINUIZED ACCELERATION FOR QUASAR CONVEX FUNCTIONS IN NON-CONVEX OPTIMIZATION |
d251648657 | We investigate whether three types of post hoc model explanations-feature attribution, concept activation, and training point ranking-are effective for detecting a model's reliance on spurious signals in the training data. Specifically, we consider the scenario where the spurious signal to be detected is unknown, at test-time, to the user of the explanation method. We design an empirical methodology that uses semi-synthetic datasets along with pre-specified spurious artifacts to obtain models that verifiably rely on these spurious training signals. We then provide a suite of metrics that assess an explanation method's reliability for spurious signal detection under various conditions. We find that the post hoc explanation methods tested are ineffective when the spurious artifact is unknown at test-time especially for non-visible artifacts like a background blur. Further, we find that feature attribution methods are susceptible to erroneously indicating dependence on spurious signals even when the model being explained does not rely on spurious artifacts. This finding casts doubt on the utility of these approaches, in the hands of a practitioner, for detecting a model's reliance on spurious signals. 1 | Published as a conference paper at ICLR 2022 POST HOC EXPLANATIONS MAY BE INEFFECTIVE FOR DETECTING UNKNOWN SPURIOUS CORRELATION |
d232146784 | Episodic and semantic memory are critical components of the human memory model. The theory of complementary learning systems(McClelland et al., 1995)suggests that the compressed representation produced by a serial event (episodic memory) is later restructured to build a more generalized form of reusable knowledge (semantic memory). In this work we develop a new principled Bayesian memory allocation scheme that bridges the gap between episodic and semantic memory via a hierarchical latent variable model. We take inspiration from traditional heap allocation and extend the idea of locally contiguous memory to the Kanerva Machine, enabling a novel differentiable block allocated latent memory. In contrast to the Kanerva Machine, we simplify the process of memory writing by treating it as a fully feed forward deterministic process, relying on the stochasticity of the read key distribution to disperse information within the memory. We demonstrate that this allocation scheme improves performance in memory conditional image generation, resulting in new state-of-the-art conditional likelihood values on binarized MNIST (≤41.58 nats/image) , binarized Omniglot (≤66.24 nats/image), as well as presenting competitive performance on CIFAR10, DMLab Mazes, Celeb-A and ImageNet32×32. | Published as a conference paper at ICLR 2021 KANERVA++: EXTENDING THE KANERVA MACHINE WITH DIFFERENTIABLE, LOCALLY BLOCK ALLOCATED LATENT MEMORY |
d4071727 | We present an integrated framework for using Convolutional Networks for classification, localization and detection. We show how a multiscale and sliding window approach can be efficiently implemented within a ConvNet. We also introduce a novel deep learning approach to localization by learning to predict object boundaries. Bounding boxes are then accumulated rather than suppressed in order to increase detection confidence. We show that different tasks can be learned simultaneously using a single shared network. This integrated framework is the winner of the localization task of the ImageNet Large Scale Visual Recognition Challenge 2013 (ILSVRC2013) and obtained very competitive results for the detection and classifications tasks. In post-competition work, we establish a new state of the art for the detection task. Finally, we release a feature extractor from our best model called OverFeat. | OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks |
d252815945 | Humans are remarkably good at understanding and reasoning about complex visual scenes. The capability to decompose low-level observations into discrete objects allows us to build a grounded abstract representation and identify the compositional structure of the world. Accordingly, it is a crucial step for machine learning models to be capable of inferring objects and their properties from visual scenes without explicit supervision. However, existing works on objectcentric representation learning either rely on tailor-made neural network modules or strong probabilistic assumptions in the underlying generative and inference processes. In this work, we present EGO, a conceptually simple and general approach to learning object-centric representations through an energy-based model. By forming a permutation-invariant energy function using vanilla attention blocks readily available in Transformers, we can infer object-centric latent variables via gradient-based MCMC methods where permutation equivariance is automatically guaranteed. We show that EGO can be easily integrated into existing architectures and can effectively extract high-quality object-centric representations, leading to better segmentation accuracy and competitive downstream task performance. Further, empirical evaluations show that EGO's learned representations are robust against distribution shift. Finally, we demonstrate the effectiveness of EGO in systematic compositional generalization, by re-composing learned energy functions for novel scene generation and manipulation.arXiv:2210.05519v1 [cs.LG] 11 Oct 2022Robust and Controllable Object-Centric Learning through Energy-based Models K V Z1 Z2Figure 1: Architecture of EGO-Attention, the variant of EGO used in experiments. x is the input image and z i are the object-centric representations. In each block, EGO attends to the latent variables to refines the hidden scene representation using cross-attention mechanism between x and z i , to measure the consistency between image input and latent representation. minimal hand-designed inductive biases. In a similar spirit, we ask whether we can learn objectcentric representations with minimal human assumptions and task-specific architectures.Contributions In this work, we introduce EGO (EnerGy-based Object-centric learning), a conceptually simple yet effective approach to learning object-centric representations without the need for specially-tailored neural network architectures or excessive generative modeling (typically parametric) assumptions. Based on the Energy-based Model (EBM) framework, we propose to learn an energy function that takes as input a visual scene and a set of object-centric latent variables and outputs a scalar value that measures the consistency between the observation and the latent representation (Section 2). We minimally assume permutation invariance among objects and embed this assumption into the energy function by leveraging the vanilla attention mechanisms from the Transformer (Vaswani et al., 2017) architecture (Section 2.1). In essence, our method makes models act as segmentation annotators, aiming to iteratively improve their annotations by minimizing our energy function. We use gradient-based Markov chain Monte Carlo (MCMC) sampling to efficiently sample latent variables from the EBM distribution, which automatically yields a permutation-equivariant update rule for the latent variables (Section 2.2). This stochastic inference procedure also addresses the inherent uncertainty in learning object-centric representations; models can learn to represent scenes containing multiple objects and potential occlusions in a probabilistic and multi-modal manner. We demonstrate the effectiveness of our approach on a variety of unsupervised object discovery tasks and show both qualitatively and qualitatively that our model can learn to decompose complex scenes into highly accurate and interpretable objects, outperforming state-of-the-art methods on segmentation performance (Section 4.1). We also show that we can reuse the learned energy functions for controllable scene generation and manipulation, which enables systematic compositional generalization to novel scenes (Section 4.2). Finally, we demonstrate the robustness of our model to various distribution shifts and hyperparameter settings (Section 4.3). | Robust and Controllable Object-Centric Learning through Energy-based Models ROBUST AND CONTROLLABLE OBJECT-CENTRIC LEARNING THROUGH ENERGY-BASED MODELS |
d86554567 | Deep Neural Networks (DNNs) are increasingly deployed in highly energyconstrained environments such as autonomous drones and wearable devices while at the same time must operate in real-time. Therefore, reducing the energy consumption has become a major design consideration in DNN training. This paper proposes the first end-to-end DNN training framework that provides quantitative energy consumption guarantees via weighted sparse projection and input masking.The key idea is to formulate the DNN training as an optimization problem in which the energy budget imposes a previously unconsidered optimization constraint. We integrate the quantitative DNN energy estimation into the DNN training process to assist the constrained optimization. We prove that an approximate algorithm can be used to efficiently solve the optimization problem. Compared to the best prior energy-saving methods, our framework trains DNNs that provide higher accuracies under same or lower energy budgets. arXiv:1806.04321v2 [cs.LG] 6 Apr 2019Published as a conference paper at ICLR 2019 ing model accuracy without incremental hyper-parameter tuning. The key idea is to formulate the DNN training process as an optimization problem in which the energy budget imposes a previously unconsidered optimization constraint. We integrate the quantitative DNN energy estimation into the DNN training process to assist the constrained optimization. In this way, a DNN model, once is trained, by design meets the energy budget while maximizing the accuracy.Without losing generality, we model the DNN energy consumption after the popular systolic array hardware architecture (Kung, 1982) that is increasingly adopted in today's DNN hardware chips such as Google's Tensor Processing Unit (TPU) (Jouppi et al., 2017), NVidia's Tensor Cores, and ARM's ML Processor. The systolic array architecture embodies key design principles of DNN hardware that is already available in today's consumer devices. We specifically focus on pruning, i.e., controlling the DNN sparsity, as the main energy reduction technique. Overall, the energy model models the DNN inference energy as a function of the sparsity of the layer parameters and the layer input. | ENERGY-CONSTRAINED COMPRESSION FOR DEEP NEURAL NETWORKS VIA WEIGHTED SPARSE PROJEC- TION AND LAYER INPUT MASKING |
d259298658 | Constraint satisfaction problems (CSPs) are about finding values of variables that satisfy the given constraints. We show that Transformer extended with recurrence is a viable approach to learning to solve CSPs in an end-to-end manner, having clear advantages over state-of-the-art methods such as Graph Neural Networks, SATNet, and some neuro-symbolic models. With the ability of Transformer to handle visual input, the proposed Recurrent Transformer can straightforwardly be applied to visual constraint reasoning problems while successfully addressing the symbol grounding problem. We also show how to leverage deductive knowledge of discrete constraints in the Transformer's inductive learning to achieve sampleefficient learning and semi-supervised learning for CSPs. | Published as a conference paper at ICLR 2023 LEARNING TO SOLVE CONSTRAINT SATISFACTION PROBLEMS WITH RECURRENT TRANSFORMER |
d211259427 | Deep networks were recently suggested to face the odds between accuracy (on clean natural images) and robustness (on adversarially perturbed images)(Tsipras et al., 2019). Such a dilemma is shown to be rooted in the inherently higher sample complexity ) and/or model capacity (Nakkiran, 2019), for learning a high-accuracy and robust classifier. In view of that, give a classification task, growing the model capacity appears to help draw a win-win between accuracy and robustness, yet at the expense of model size and latency, therefore posing challenges for resource-constrained applications. Is it possible to co-design model accuracy, robustness and efficiency to achieve their triple wins? This paper studies multi-exit networks associated with input-adaptive efficient inference, showing their strong promise in achieving a "sweet point" in cooptimizing model accuracy, robustness and efficiency. Our proposed solution, dubbed Robust Dynamic Inference Networks (RDI-Nets), allows for each input (either clean or adversarial) to adaptively choose one of the multiple output layers (early branches or the final one) to output its prediction. That multi-loss adaptivity adds new variations and flexibility to adversarial attacks and defenses, on which we present a systematical investigation. We show experimentally that by equipping existing backbones with such robust adaptive inference, the resulting RDI-Nets can achieve better accuracy and robustness, yet with over 30% computational savings, compared to the defended original models.Published as a conference paper at ICLR 2020 and on a given dataset, such trade-off might be effectively alleviated ("win-win"), if supplying more training data and/or replacing a larger-capacity classifier.On a separate note, deep networks also face the pressing challenge to be deployed on resourceconstrained platforms due to the prosperity of smart Internet-of-Things (IoT) devices. Many IoT applications naturally demand security and trustworthiness, e.g., , biometrics and identity verification, but can only afford limited latency, memory and energy budget. Hereby we extend the question: can we achieve a triple-win, i.e., , an accurate and robust classfier while keeping it efficient?This paper makes an attempt in providing a positive answer to the above question. Rather than proposing a specific design of robust light-weight models, we reduce the average computation loads by input-adaptive routing to achieve triple-win. To this end, we introduce the input-adaptive dynamic inference(Teerapittayanon et al., 2017;Wang et al., 2018a), an emerging efficient inference scheme in contrast to the (non-adaptive) model compression, to the adversarial defense field for the first time. Given any deep network backbone (e.g., , ResNet, MobileNet), we first follow(Teerapittayanon et al., 2017)to augment it with multiple early-branch output layers in addition to the original final output. Each input, regardless of clean or adversarial samples, adaptively chooses which output layer to take for its own prediction. Therefore, a large portion of input inferences can be terminated early when the samples can already be inferred with high confidence. | Published as a conference paper at ICLR 2020 TRIPLE WINS: BOOSTING ACCURACY, ROBUSTNESS AND EFFICIENCY TOGETHER BY ENABLING INPUT- ADAPTIVE INFERENCE |
d208527060 | Recent advances in the sparse neural network literature have made it possible to prune many large feed forward and convolutional networks with only a small quantity of data. Yet, these same techniques often falter when applied to the problem of recovering sparse recurrent networks. These failures are quantitative: when pruned with recent techniques, RNNs typically obtain worse performance than they do under a simple random pruning scheme. The failures are also qualitative: the distribution of active weights in a pruned LSTM or GRU network tend to be concentrated in specific neurons and gates, and not well dispersed across the entire architecture. We seek to rectify both the quantitative and qualitative issues with recurrent network pruning by introducing a new recurrent pruning objective derived from the spectrum of the recurrent Jacobian. Our objective is data efficient (requiring only 64 data points to prune the network), easy to implement, and produces 95 % sparse GRUs that significantly improve on existing baselines. We evaluate on sequential MNIST, Billion Words, and Wikitext. | ONE-SHOT PRUNING OF RECURRENT NEURAL NET- WORKS BY JACOBIAN SPECTRUM EVALUATION |
d12268909 | Neural language models learn word representations, or embeddings, that capture rich linguistic and conceptual information. Here we investigate the embeddings learned by neural machine translation models, a recently-developed class of neural language model. We show that embeddings from translation models outperform those learned by monolingual models at tasks that require knowledge of both conceptual similarity and lexical-syntactic role. We further show that these effects hold when translating from both English to French and English to German, and argue that the desirable properties of translation embeddings should emerge largely independently of the source and target languages. Finally, we apply a new method for training neural translation models with very large vocabularies, and show that this vocabulary expansion algorithm results in minimal degradation of embedding quality. Our embedding spaces can be queried in an online demo and downloaded from our web page. Overall, our analyses indicate that translation-based embeddings should be used in applications that require concepts to be organised according to similarity and/or lexical function, while monolingual embeddings are better suited to modelling (nonspecific) inter-word relatedness. | EMBEDDING WORD SIMILARITY WITH NEURAL MACHINE TRANSLATION |
d257557257 | Contrastive learning is a cornerstone underlying recent progress in multi-view and multimodal learning, e.g., in representation learning with image/caption pairs. While its effectiveness is not yet fully understood, a line of recent work reveals that contrastive learning can invert the data generating process and recover ground truth latent factors shared between views. In this work, we present new identifiability results for multimodal contrastive learning, showing that it is possible to recover shared factors in a more general setup than the multi-view setting studied previously. Specifically, we distinguish between the multi-view setting with one generative mechanism (e.g., multiple cameras of the same type) and the multimodal setting that is characterized by distinct mechanisms (e.g., cameras and microphones). Our work generalizes previous identifiability results by redefining the generative process in terms of distinct mechanisms with modality-specific latent variables. We prove that contrastive learning can block-identify latent factors shared between modalities, even when there are nontrivial dependencies between factors. We empirically verify our identifiability results with numerical simulations and corroborate our findings on a complex multimodal dataset of image/text pairs. Zooming out, our work provides a theoretical basis for multimodal representation learning and explains in which settings multimodal contrastive learning can be effective in practice. | Published as a conference paper at ICLR 2023 IDENTIFIABILITY RESULTS FOR MULTIMODAL CONTRASTIVE LEARNING |
d252683720 | For long-tailed classification tasks, most works often pretrain a big model on a large-scale (unlabeled) dataset, and then fine-tune the whole pretrained model for adapting to long-tailed data. Though promising, fine-tuning the whole pretrained model tends to suffer from high cost in computation and deployment of different models for different tasks, as well as weakened generalization capability for overfitting to certain features of long-tailed data. To alleviate these issues, we propose an effective Long-tailed Prompt Tuning (LPT) method for long-tailed classification tasks. LPT introduces several trainable prompts into a frozen pretrained model to adapt it to long-tailed data. For better effectiveness, we divide prompts into two groups: 1) a shared prompt for the whole long-tailed dataset to learn general features and to adapt a pretrained model into the target long-tailed domain; and 2) group-specific prompts to gather group-specific features for the samples which have similar features and also to empower the pretrained model with fine-grained discrimination ability. Then we design a two-phase training paradigm to learn these prompts. In the first phase, we train the shared prompt via conventional supervised prompt tuning to adapt a pretrained model to the desired long-tailed domain. In the second phase, we use the learnt shared prompt as query to select a small best matched set for a group of similar samples from the group-specific prompt set to dig the common features of these similar samples, and then optimize these prompts with a dual sampling strategy and the asymmetric Gaussian Clouded Logit loss. By only fine-tuning a few prompts while fixing the pretrained model, LPT can reduce training cost and deployment cost by storing a few prompts, and enjoys a strong generalization ability of the pretrained model. Experiments show that on various long-tailed benchmarks, with only ∼1.1% extra trainable parameters, LPT achieves comparable or higher performance than previous whole model fine-tuning methods, and is more robust to domain-shift. Code is publicly available at https://github.com/DongSky/LPT. | Published as a conference paper at ICLR 2023 LPT: LONG-TAILED PROMPT TUNING FOR IMAGE CLASSIFICATION |
d249848305 | The Receptive Field (RF) size has been one of the most important factors for One Dimensional Convolutional Neural Networks (1D-CNNs) on time series classification tasks. Large efforts have been taken to choose the appropriate size because it has a huge influence on the performance and differs significantly for each dataset. In this paper, we propose an Omni-Scale block (OS-block) for 1D-CNNs, where the kernel sizes are decided by a simple and universal rule. Particularly, it is a set of kernel sizes that can efficiently cover the best RF size across different datasets via consisting of multiple prime numbers according to the length of the time series. The experiment result shows that models with the OS-block can achieve a similar performance as models with the searched optimal RF size and due to the strong optimal RF size capture ability, simple 1D-CNN models with OS-block achieves the state-of-the-art performance on four time series benchmarks, including both univariate and multivariate data from multiple domains. Comprehensive analysis and discussions shed light on why the OS-block can capture optimal RF sizes across different datasets. Code available here 1 | OMNI-SCALE CNNS: A SIMPLE AND EFFECTIVE KER- NEL SIZE CONFIGURATION FOR TIME SERIES CLASSIFI- CATION |
d257427177 | Existing large language model-based code generation pipelines typically use beam search or sampling algorithms during the decoding process. Although the programs they generate achieve high token-matching-based scores, they often fail to compile or generate incorrect outputs. The main reason is that conventional Transformer decoding algorithms may not be the best choice for code generation. In this work, we propose a novel Transformer decoding algorithm, Planning-Guided Transformer Decoding (PG-TD), that uses a planning algorithm to do lookahead search and guide the Transformer to generate better programs. Specifically, instead of simply optimizing the likelihood of the generated sequences, the Transformer makes use of a planner to generate candidate programs and test them on public test cases. The Transformer can therefore make more informed decisions and generate tokens that will eventually lead to higher-quality programs. We also design a mechanism that shares information between the Transformer and the planner to make our algorithm computationally efficient. We empirically evaluate our framework with several large language models as backbones on public coding challenge benchmarks, showing that 1) it can generate programs that consistently achieve higher performance compared with competing baseline methods; 2) it enables controllable code generation, such as concise codes and highly-commented codes by optimizing modified objective 1 . | Published as a conference paper at ICLR 2023 PLANNING WITH LARGE LANGUAGE MODELS FOR CODE GENERATION |
d254125471 | Kernel matrices, as well as weighted graphs represented by them, are ubiquitous objects in machine learning, statistics and other related fields. The main drawback of using kernel methods (learning and inference using kernel matrices) is efficiency -given n input points, most kernel-based algorithms need to materialize the full n × n kernel matrix before performing any subsequent computation, thus incurring Ω(n 2 ) runtime. Breaking this quadratic barrier for various problems has therefore, been a subject of extensive research efforts.We break the quadratic barrier and obtain subquadratic time algorithms for several fundamental linear-algebraic and graph processing primitives, including approximating the top eigenvalue and eigenvector, spectral sparsification, solving linear systems, local clustering, lowrank approximation, arboricity estimation and counting weighted triangles. We build on the recently developed Kernel Density Estimation framework, which (after preprocessing in time subquadratic in n) can return estimates of row/column sums of the kernel matrix. In particular, we develop efficient reductions from weighted vertex and weighted edge sampling on kernel graphs, simulating random walks on kernel graphs, and importance sampling on matrices to Kernel Density Estimation and show that we can generate samples from these distributions in sublinear (in the support of the distribution) time. Our reductions are the central ingredient in each of our applications and we believe they may be of independent interest. We empirically demonstrate the efficacy of our algorithms on low-rank approximation (LRA) and spectral sparsification, where we observe a 9x decrease in the number of kernel evaluations over baselines for LRA and a 41x reduction in the graph size for spectral sparsification.we design such algorithms for problems such as eigenvalue/eigenvector estimation, low-rank approximation, graph sparsification, local clustering, aboricity estimation, and estimating the total weight of triangles.Our results are obtained via the following two-pronged approach. First, we use KDE data structures to design algorithms for the following basic primitives, frequently used in sublinear time algorithms and property testing: | Sub-quadratic Algorithms for Kernel Matrices via Kernel Density Estimation |
d247158489 | As Artificial Intelligence as a Service gains popularity, protecting well-trained models as intellectual property is becoming increasingly important. There are two common types of protection methods: ownership verification and usage authorization. In this paper, we propose Non-Transferable Learning (NTL), a novel approach that captures the exclusive data representation in the learned model and restricts the model generalization ability to certain domains. This approach provides effective solutions to both model verification and authorization. Specifically: 1) For ownership verification, watermarking techniques are commonly used but are often vulnerable to sophisticated watermark removal methods. By comparison, our NTL-based ownership verification provides robust resistance to stateof-the-art watermark removal methods, as shown in extensive experiments with 6 removal approaches over the digits, CIFAR10 & STL10, and VisDA datasets. 2) For usage authorization, prior solutions focus on authorizing specific users to access the model, but authorized users can still apply the model to any data without restriction. Our NTL-based authorization approach instead provides data-centric protection, which we call applicability authorization, by significantly degrading the performance of the model on unauthorized data. Its effectiveness is also shown through experiments on aforementioned datasets. . Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pp. 248-255. Ieee, 2009. Li Deng. The mnist database of handwritten digit images for machine learning research [best of the web]. . What can be transferred: Unsupervised domain adaptation for endoscopic lesions segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 4023-4032, 2020. Jiahua Dong, Yang Cong, Gan Sun, Zhen Fang, and Zhengming Ding. Where and how to transfer: knowledge aggregation-induced transferability perception for unsupervised domain adaptation. | NON-TRANSFERABLE LEARNING: A NEW APPROACH FOR MODEL OWNERSHIP VERIFICATION AND APPLI- CABILITY AUTHORIZATION |
d222133157 | Intelligent agents need to generalize from past experience to achieve goals in complex environments. World models facilitate such generalization and allow learning behaviors from imagined outcomes to increase sample-efficiency. While learning world models from image inputs has recently become feasible for some tasks, modeling Atari games accurately enough to derive successful behaviors has remained an open challenge for many years. We introduce DreamerV2, a reinforcement learning agent that learns behaviors purely from predictions in the compact latent space of a powerful world model. The world model uses discrete representations and is trained separately from the policy. DreamerV2 constitutes the first agent that achieves human-level performance on the Atari benchmark of 55 tasks by learning behaviors inside a separately trained world model. With the same computational budget and wall-clock time, Dreamer V2 reaches 200M frames and surpasses the final performance of the top single-GPU agents IQN and Rainbow. DreamerV2 is also applicable to tasks with continuous actions, where it learns an accurate world model of a complex humanoid robot and solves stand-up and walking from only pixel inputs. | Published as a conference paper at ICLR 2021 MASTERING ATARI WITH DISCRETE WORLD MODELS |
d256459301 | A fundamental challenge for machine learning models is how to generalize learned models for out-of-distribution (OOD) data. Among various approaches, exploiting invariant features by Domain Adversarial Training (DAT) received widespread attention. Despite its success, we observe training instability from DAT, mostly due to over-confident domain discriminator and environment label noise. To address this issue, we proposed Environment Label Smoothing (ELS), which encourages the discriminator to output soft probability, which thus reduces the confidence of the discriminator and alleviates the impact of noisy environment labels. We demonstrate, both experimentally and theoretically, that ELS can improve training stability, local convergence, and robustness to noisy environment labels. By incorporating ELS with DAT methods, we are able to yield the state-of-art results on a wide range of domain generalization/adaptation tasks, particularly when the environment labels are highly noisy. The code is avaliable at https://github.com/yfzhang114/Environment-Label-Smoothing. * Work done during an internship at Alibaba Group. † Work done at Alibaba Group, and now affiliated with Twitter.Published as a conference paper at ICLR 2023 Proposition 1. Given two domain distributions D S , D T over X, and a hypothesis class H. We supposeĥ ∈Ĥ the optimal discriminator with no constraint, denote the mixed distributions withCompared to Proposition 2 in (Acuna et al., 2021) that adversarial training in DANN is equal to minimize 2D JS (D S D T ) − 2 log 2. The only difference here is the mixed distributions D S ′ , D T ′ , which allows more flexible control on divergence minimization. For example, when γ = 1, D S ′ = D S , D T ′ = D T which is the same as the original adversarial training; when γ = 0.5, D S ′ = D T ′ = 0.5(D S + D T ) and D JS (D S ′ D T ′ ) = 0, which means that this term will not supply gradients during training and the training process will convergence like ERM. In other words, γ controls the tradeoff between algorithm convergence and adversarial divergence minimization. One main argue that adjusting the tradeoff weight λ can also balance AT and ERM, however, λ can only adjust the gradient contribution of AT part, i.e., 2λ∇D JS (D S , D T ) and cannot affect the training dynamic of D JS (D S , D T ). For example, when D S , D T have disjoint support, ∇D JS (D S , D T ) is always zero no matter what λ is given. On the contrary, the proposed technique smooths the optimization distribution D S , D T of AT, making the whole training process more stable, but controlling λ cannot do. In the experimental section, we show that in some benchmarks, the model cannot converge even if the tradeoff weight is small enough, however, when ELS is applied, DANN+ELS attains superior results and without the need for small tradeoff weights or small learning rate.As shown in (Goodfellow, 2016), GANs always use a technique called one-sided label smoothing, which is a simple modification of the label smoothing technique and only replaces the target for real examples with a value slightly less than one, such as 0.9. Here we connect one-sided label smoothing to JS divergence and seek the difference between native and one-sided label smoothing techniques. See Appendix A.2 for proof and analysis. We further extend the above theoretical analysis to multi-domain settings, e.g., domain generalization, and multi-source GANs (Trung Le et al., 2019) (See Proposition 3 in Appendix A.3 for detailed proof and analysis.). We find that with ELS, a flexible control on algorithm convergence and divergence minimization tradeoffs can be attained. . Deep learning with convolutional neural networks for eeg decoding and visualization. Human brain mapping, 38(11):5391-5420, 2017. . Stabilizing adversarial nets with prediction methods. arXiv preprint arXiv:1705.07364, 2017. | Published as a conference paper at ICLR 2023 FREE LUNCH FOR DOMAIN ADVERSARIAL TRAINING: ENVIRONMENT LABEL SMOOTHING |
d237386137 | Recent years have witnessed a surging interest in Neural Architecture Search (NAS). Various algorithms have been proposed to improve the search efficiency and the search effectiveness of NAS, i.e., to reduce the search cost and improve the generalization performance of the selected architectures, respectively. However, the search efficiency of these algorithms is severely limited by the need for model training during the search process. To overcome this limitation, we propose a novel NAS algorithm called NAS at Initialization (NASI) that exploits the capability of a Neural Tangent Kernel in being able to characterize the performance of candidate architectures at initialization, hence allowing model training to be completely avoided to boost the search efficiency. Besides the improved search efficiency, NASI also achieves competitive search effectiveness on various datasets like CIFAR-10/100 and ImageNet. Further, NASI is shown to be labeland data-agnostic under mild conditions, which guarantees the transferability of the architectures selected by our NASI over different datasets.arXiv:2109.00817v2 [cs.LG] 25 Apr 2022Published as a conference paper at ICLR 2022 the estimated performance of candidate architectures by NTK, NAS can be reformulated into an optimization problem without model training (Sec. 3.1). However, NTK is prohibitively costly to evaluate. Fortunately, we can approximate it 1 using a similar form to gradient flow (Wang et al., 2020) (Sec. 3.2). This results in a reformulated NAS problem that can be solved efficiently by a gradient-based algorithm via additional relaxation using Gumbel-Softmax (Jang et al., 2017) (Sec. 3.3). Interestingly, NASI is even shown to be labeland data-agnostic under mild conditions, which thus implies the transferability of the architectures selected by NASI over different datasets (Sec. 4).We will firstly empirically demonstrate the improved search efficiency and the competitive search effectiveness achieved by NASI in NAS-Bench-1Shot1 (Zela et al., 2020b) (Sec. 5.1). Compared with other NAS algorithms, NASI incurs the smallest search cost while preserving the competitive performance of its selected architectures. Meanwhile, the architectures selected by NASI from the DARTS (Liu et al., 2019) search space over CIFAR-10 consistently enjoy the competitive or even outperformed performance when evaluated on different benchmark datasets, e.g., CIFAR-10/100 and ImageNet (Sec. 5.2), indicating the guaranteed transferability of architectures selected by our NASI. In Sec. 5.3, NASI is further demonstrated to be able to select well-performing architectures on CIFAR-10 even with randomly generated labels or data, which strongly supports the labeland data-agnostic search and therefore the guaranteed transferability achieved by our NASI. | Published as a conference paper at ICLR 2022 NASI: LABEL-AND DATA-AGNOSTIC NEURAL ARCHITECTURE SEARCH AT INITIALIZATION |
d258352681 | Robots operating in the real world require both rich manipulation skills as well as the ability to semantically reason about when to apply those skills. Towards this goal, recent works have integrated semantic representations from large-scale pretrained vision-language (VL) models into manipulation models, imparting them with more general reasoning capabilities. However, we show that the conventional pretraining-finetuning pipeline for integrating such representations entangles the learning of domain-specific action information and domain-general visual information, leading to less data-efficient training and poor generalization to unseen objects and tasks. To this end, we propose PROGRAMPORT, a modular approach to better leverage pretrained VL models by exploiting the syntactic and semantic structures of language instructions. Our framework uses a semantic parser to recover an executable program, composed of functional modules grounded on vision and action across different modalities. Each functional module is realized as a combination of deterministic computation and learnable neural networks. Program execution produces parameters to general manipulation primitives for a robotic end-effector. The entire modular network can be trained with end-to-end imitation learning objectives. Experiments show that our model successfully disentangles action and perception, translating to improved zero-shot and compositional generalization in a variety of manipulation behaviors. Project | Published as a conference paper at ICLR 2023 PROGRAMMATICALLY GROUNDED, COMPOSITION- ALLY GENERALIZABLE ROBOTIC MANIPULATION |
d2647832 | We introduce Deep Linear Discriminant Analysis (DeepLDA) which learns linearly separable latent representations in an end-to-end fashion. Classic LDA extracts features which preserve class separability and is used for dimensionality reduction for many classification problems. The central idea of this paper is to put LDA on top of a deep neural network. This can be seen as a non-linear extension of classic LDA. Instead of maximizing the likelihood of target labels for individual samples, we propose an objective function that pushes the network to produce feature distributions which: (a) have low variance within the same class and (b) high variance between different classes. Our objective is derived from the general LDA eigenvalue problem and still allows to train with stochastic gradient descent and back-propagation. For evaluation we test our approach on three different benchmark datasets (MNIST, CIFAR-10 and STL-10). DeepLDA produces competitive results on MNIST and CIFAR-10 and outperforms a network trained with categorical cross entropy (having the same architecture) on a supervised setting of STL-10. * | DEEP LINEAR DISCRIMINANT ANALYSIS |
d204874971 | Learning rich representation from data is an important task for deep generative models such as variational auto-encoder (VAE). However, by extracting high-level abstractions in the bottom-up inference process, the goal of preserving all factors of variations for top-down generation is compromised. Motivated by the concept of "starting small", we present a strategy to progressively learn independent hierarchical representations from high-to low-levels of abstractions. The model starts with learning the most abstract representation, and then progressively grow the network architecture to introduce new representations at different levels of abstraction. We quantitatively demonstrate the ability of the presented model to improve disentanglement in comparison to existing works on two benchmark data sets using three disentanglement metrics, including a new metric we proposed to complement the previously-presented metric of mutual information gap. We further present both qualitative and quantitative evidence on how the progression of learning improves disentangling of hierarchical representations. By drawing on the respective advantage of hierarchical representation learning and progressive learning, this is to our knowledge the first attempt to improve disentanglement by progressively growing the capacity of VAE to learn hierarchical representations 1 . | Published as a conference paper at ICLR 2020 PROGRESSIVE LEARNING AND DISENTANGLEMENT OF HIERARCHICAL REPRESENTATIONS |
d208176134 | Handling missing data is one of the most fundamental problems in machine learning. Among many approaches, the simplest and most intuitive way is zero imputation, which treats the value of a missing entry simply as zero. However, many studies have experimentally confirmed that zero imputation results in suboptimal performances in training neural networks. Yet, none of the existing work has explained what brings such performance degradations. In this paper, we introduce the variable sparsity problem (VSP), which describes a phenomenon where the output of a predictive model largely varies with respect to the rate of missingness in the given input, and show that it adversarially affects the model performance. We first theoretically analyze this phenomenon and propose a simple yet effective technique to handle missingness, which we refer to as Sparsity Normalization (SN), that directly targets and resolves the VSP. We further experimentally validate SN on diverse benchmark datasets, to show that debiasing the effect of input-level sparsity improves the performance and stabilizes the training of neural networks. | Published as a conference paper at ICLR 2020 WHY NOT TO USE ZERO IMPUTATION? CORRECTING SPARSITY BIAS IN TRAINING NEURAL NETWORKS |
d244908227 | Vision Transformer (ViT) and its variants (e.g., Swin, PVT) have achieved great success in various computer vision tasks, owing to their capability to learn longrange contextual information. Layer Normalization (LN) is an essential ingredient in these models. However, we found that the ordinary LN makes tokens at different positions similar in magnitude because it normalizes embeddings within each token. It is difficult for Transformers to capture inductive bias such as the positional context in an image with LN. We tackle this problem by proposing a new normalizer, termed Dynamic Token Normalization (DTN), where normalization is performed both within each token (intra-token) and across different tokens (intertoken). DTN has several merits. Firstly, it is built on a unified formulation and thus can represent various existing normalization methods. Secondly, DTN learns to normalize tokens in both intra-token and inter-token manners, enabling Transformers to capture both the global contextual information and the local positional context. Thirdly, by simply replacing LN layers, DTN can be readily plugged into various vision transformers, such as ViT, Swin, PVT, LeViT, T2T-ViT, BigBird and Reformer. Extensive experiments show that the transformer equipped with DTN consistently outperforms baseline model with minimal extra parameters and computational overhead. For example, DTN outperforms LN by 0.5% -1.2% top-1 accuracy on ImageNet, by 1.2 -1.4 box AP in object detection on COCO benchmark, by 2.3% -3.9% mCE in robustness experiments on ImageNet-C, and by 0.5% -0.8% accuracy in Long ListOps on Long-Range Arena. Codes will be made public at https://github.com/wqshao126/DTN. | DYNAMIC TOKEN NORMALIZATION IMPROVES VI- SION TRANSFORMER |
d257496643 | Self-paced learning has been beneficial for tasks where some initial knowledge is available, such as weakly supervised learning and domain adaptation, to select and order the training sample sequence, from easy to complex. However its applicability remains unexplored in unsupervised learning, whereby the knowledge of the task matures during training. We propose a novel HYperbolic Self-Paced model (HYSP) for learning skeletonbased action representations. HYSP adopts self-supervision: it uses data augmentations to generate two views of the same sample, and it learns by matching one (named online) to the other (the target). We propose to use hyperbolic uncertainty to determine the algorithmic learning pace, under the assumption that less uncertain samples should be more strongly driving the training, with a larger weight and pace. Hyperbolic uncertainty is a by-product of the adopted hyperbolic neural networks, it matures during training and it comes with no extra cost, compared to the established Euclidean SSL framework counterparts. When tested on three established skeleton-based action recognition datasets, HYSP outperforms the state-of-the-art on PKU-MMD I, as well as on 2 out of 3 downstream tasks on NTU-60 and NTU-120. Additionally, HYSP only uses positive pairs and bypasses therefore the complex and computationally-demanding mining procedures required for the negatives in contrastive techniques. Code is available at https | HYPERBOLIC SELF-PACED LEARNING FOR SELF- SUPERVISED SKELETON-BASED ACTION REPRESEN- TATIONS |
d232110691 | We propose a new simple approach for image compression: instead of storing the RGB values for each pixel of an image, we store the weights of a neural network overfitted to the image. Specifically, to encode an image, we fit it with an MLP which maps pixel locations to RGB values. We then quantize and store the weights of this MLP as a code for the image. To decode the image, we simply evaluate the MLP at every pixel location. We found that this simple approach outperforms JPEG at low bit-rates, even without entropy coding or learning a distribution over weights. While our framework is not yet competitive with state of the art compression methods, we show that it has various attractive properties which could make it a viable alternative to other neural data compression approaches. | COIN: COMPRESSION WITH IMPLICIT NEURAL REPRESENTATIONS |
d232427971 | We consider the task of enforcing individual fairness in gradient boosting. Gradient boosting is a popular method for machine learning from tabular data, which arise often in applications where algorithmic fairness is a concern. At a high level, our approach is a functional gradient descent on a (distributionally) robust loss function that encodes our intuition of algorithmic fairness for the ML task at hand. Unlike prior approaches to individual fairness that only work with smooth ML models, our approach also works with non-smooth models such as decision trees. We show that our algorithm converges globally and generalizes. We also demonstrate the efficacy of our algorithm on three ML problems susceptible to algorithmic bias. | Published as a conference paper at ICLR 2021 INDIVIDUALLY FAIR GRADIENT BOOSTING |
d16359184 | Learning Stable Group Invariant Representations with Convolutional Networks | |
d230084048 | Neural Ordinary Differential Equations (NODEs) use a neural network to model the instantaneous rate of change in the state of a system. However, despite their apparent suitability for dynamics-governed time-series, NODEs present a few disadvantages. First, they are unable to adapt to incoming data points, a fundamental requirement for real-time applications imposed by the natural direction of time. Second, time series are often composed of a sparse set of measurements that could be explained by many possible underlying dynamics. NODEs do not capture this uncertainty. In contrast, Neural Processes (NPs) are a family of models providing uncertainty estimation and fast data adaptation but lack an explicit treatment of the flow of time. To address these problems, we introduce Neural ODE Processes (NDPs), a new class of stochastic processes determined by a distribution over Neural ODEs. By maintaining an adaptive data-dependent distribution over the underlying ODE, we show that our model can successfully capture the dynamics of low-dimensional systems from just a few data points. At the same time, we demonstrate that NDPs scale up to challenging high-dimensional time-series with unknown latent dynamics such as rotating MNIST digits. * Equal contribution. † Work done as an AI Resident at the University of Cambridge. | Published as a conference paper at ICLR 2021 NEURAL ODE PROCESSES |
d260429228 | Sequences have become first class citizens in supervised learning thanks to the resurgence of recurrent neural networks. Many complex tasks that require mapping from or to a sequence of observations can now be formulated with the sequence-to-sequence (seq2seq) framework which employs the chain rule to efficiently represent the joint probability of sequences. In many cases, however, variable sized inputs and/or outputs might not be naturally expressed as sequences. For instance, it is not clear how to input a set of numbers into a model where the task is to sort them; similarly, we do not know how to organize outputs when they correspond to random variables and the task is to model their unknown joint probability. In this paper, we first show using various examples that the order in which we organize input and/or output data matters significantly when learning an underlying model. We then discuss an extension of the seq2seq framework that goes beyond sequences and handles input sets in a principled way. In addition, we propose a loss which, by searching over possible orders during training, deals with the lack of structure of output sets. We show empirical evidence of our claims regarding ordering, and on the modifications to the seq2seq framework on benchmark language modeling and parsing tasks, as well as two artificial tasks -sorting numbers and estimating the joint probability of unknown graphical models. | ORDER MATTERS: SEQUENCE TO SEQUENCE FOR SETS |
d53107519 | The convergence rate and final performance of common deep learning models have significantly benefited from heuristics such as learning rate schedules, knowledge distillation, skip connections, and normalization layers. In the absence of theoretical underpinnings, controlled experiments aimed at explaining these strategies can aid our understanding of deep learning landscapes and the training dynamics. Existing approaches for empirical analysis rely on tools of linear interpolation and visualizations with dimensionality reduction, each with their limitations. Instead, we revisit such analysis of heuristics through the lens of recently proposed methods for loss surface and representation analysis, viz., mode connectivity and canonical correlation analysis (CCA), and hypothesize reasons for the success of the heuristics. In particular, we explore knowledge distillation and learning rate heuristics of (cosine) restarts and warmup using mode connectivity and CCA. Our empirical analysis suggests that: (a) the reasons often quoted for the success of cosine annealing are not evidenced in practice; (b) that the effect of learning rate warmup is to prevent the deeper layers from creating training instability; and (c) that the latent knowledge shared by the teacher is primarily disbursed to the deeper layers. | A CLOSER LOOK AT DEEP LEARNING HEURISTICS: LEARNING RATE RESTARTS, WARMUP AND DISTILLA- TION |
d202712906 | An important research direction in machine learning has centered around developing meta-learning algorithms to tackle few-shot learning. An especially successful algorithm has been Model Agnostic Meta-Learning (MAML), a method that consists of two optimization loops, with the outer loop finding a meta-initialization, from which the inner loop can efficiently learn new tasks. Despite MAML's popularity, a fundamental open question remains -is the effectiveness of MAML due to the meta-initialization being primed for rapid learning (large, efficient changes in the representations) or due to feature reuse, with the meta-initialization already containing high quality features? We investigate this question, via ablation studies and analysis of the latent representations, finding that feature reuse is the dominant factor. This leads to the ANIL (Almost No Inner Loop) algorithm, a simplification of MAML where we remove the inner loop for all but the (task-specific) head of the underlying neural network. ANIL matches MAML's performance on benchmark few-shot image classification and RL and offers computational improvements over MAML. We further study the precise contributions of the head and body of the network, showing that performance on the test tasks is entirely determined by the quality of the learned features, and we can remove even the head of the network (the NIL algorithm). We conclude with a discussion of the rapid learning vs feature reuse question for meta-learning algorithms more broadly. * Equal contribution arXiv:1909.09157v2 [cs.LG] . Recasting gradientbased meta-learning as hierarchical bayes. arXiv preprint arXiv:1801.08930, 2018.James Harrison, Apoorva Sharma, and Marco Pavone. Meta-learning priors for efficient online bayesian regression. arXiv preprint arXiv:1807.08912, 2018. . Gradient-based meta-learning with learned layerwise metric and subspace. arXiv preprint arXiv:1801.05558, 2018. | Published as a conference paper at ICLR 2020 RAPID LEARNING OR FEATURE REUSE? TOWARDS UNDERSTANDING THE EFFECTIVENESS OF MAML |
d246411266 | Empirical risk minimization (ERM) is known to be non-robust in practice to distributional shift where the training and the test distributions are different. A suite of approaches, such as importance weighting, and variants of distributionally robust optimization (DRO), have been proposed to solve this problem. But a line of recent work has empirically shown that these approaches do not significantly improve over ERM in real applications with distribution shift. The goal of this work is to obtain a comprehensive theoretical understanding of this intriguing phenomenon. We first posit the class of Generalized Reweighting (GRW) algorithms, as a broad category of approaches that iteratively update model parameters based on iterative reweighting of the training samples. We show that when overparameterized models are trained under GRW, the resulting models are close to that obtained by ERM. We also show that adding small regularization which does not greatly affect the empirical training accuracy does not help. Together, our results show that a broad category of what we term GRW approaches are not able to achieve distributionally robust generalization. Our work thus has the following sobering takeaway: to make progress towards distributionally robust generalization, we either have to develop non-GRW approaches, or perhaps devise novel classification/regression loss functions that are adapted to GRW approaches. 1 Our results can be easily extended to the multi-class scenario (see Appendix B). 2 For distributions P and Q, Q is absolute continuous to P , or Q P , means that for any event A, P (A) = 0 implies Q(A) = 0. | Published as a conference paper at ICLR 2023 UNDERSTANDING WHY GENERALIZED REWEIGHTING DOES NOT IMPROVE OVER ERM |
d253155221 | When learning task-oriented dialogue (ToD) agents, reinforcement learning (RL) techniques can naturally be utilized to train dialogue strategies to achieve userspecific goals. Prior works mainly focus on adopting advanced RL techniques to train the ToD agents, while the design of the reward function is not well studied. This paper aims at answering the question of how to efficiently learn and leverage a reward function for training end-to-end (E2E) ToD agents. Specifically, we introduce two generalized objectives for reward-function learning, inspired by the classical learning-to-rank literature. Further, we utilize the learned reward function to guide the training of the E2E ToD agent. With the proposed techniques, we achieve competitive results on the E2E response-generation task on the Multiwoz 2.0 dataset. Source code and checkpoints are publicly released at https://github.com/Shentao-YANG/Fantastic Reward ICLR2023. * Equal Contribution. Corresponds to | Published as a conference paper at ICLR 2023 FANTASTIC REWARDS AND HOW TO TAME THEM: A CASE STUDY ON REWARD LEARNING FOR TASK- ORIENTED DIALOGUE SYSTEMS |
d16142207 | In this paper, we propose a data representation model that demonstrates hierarchical feature learning using nsNMF. We extend unit algorithm into several layers. Experiments with document and image data successfully discovered feature hierarchies. We also prove that proposed method results in much better classification and reconstruction performance, especially for small number of features. | Hierarchical Data Representation Model - Multi-layer NMF |
d239768649 | In this work, we propose a communication-efficient parameterization, FedPara, for federated learning (FL) to overcome the burdens on frequent model uploads and downloads. Our method re-parameterizes weight parameters of layers using low-rank weights followed by the Hadamard product. Compared to the conventional low-rank parameterization, our FedPara method is not restricted to lowrank constraints, and thereby it has a far larger capacity. This property enables to achieve comparable performance while requiring 3 to 10 times lower communication costs than the model with the original layers, which is not achievable by the traditional low-rank methods. The efficiency of our method can be further improved by combining with other efficient FL optimizers. In addition, we extend our method to a personalized FL application, pFedPara, which separates parameters into global and local ones. We show that pFedPara outperforms competing personalized FL methods with more than three times fewer parameters. Project page: https://github.com/South-hw/FedPara_ICLR22• Heterogeneous data. Data is decentralized and non-IID as well as unbalanced in its amount due to different characteristics of clients; thus, local data does not represent the population distribution.• Heterogeneous systems. Clients consist of heterogeneous setups of hardware and infrastructure; hence, those connections are not guaranteed to be online, fast, or cheap. Besides, massive client participation is expected through different communication paths, causing communication burdens.These FL properties introduce challenges in the convergence stability with heterogeneous data and communication overheads. To improve the stability and reduce the communication rounds, the prior works in FL have proposed modified loss functions or model aggregation methods (Li et al., 2020; Karimireddy et al., 2020; Acar et al., 2021; Yu et al., 2020; Reddi et al., 2021). However, the transferred data is still a lot for the edge devices with bandwidth constraints or countries having low-quality communication infrastructure. 1 A large amount of transferred data introduces an energy consumption issue on edge devices because wireless communication is significantly more powerintensive than computation (Yadav & Yadav, 2016; Yan et al., 2019). 1 The gap between the fastest and the lowest communication speed across countries is significant; approximately 63 times different (Speedtest).arXiv:2108.06098v3 [cs.LG] 19 Jan 2023Published as a conference paper at ICLR 2022Notations. We denote the Hadamard product as , the Kronecker product as ⊗, n-mode tensor product as × n , and the i-th unfolding of the tensor T (i) ∈ R ki× j =i kj given a tensor T ∈ R k1×···×kn .OVERVIEW OF LOW-RANK PARAMETERIZATIONThe low-rank decomposition in neural networks has been typically applied to pre-trained models for compression (Phan et al., 2020), whereby the number of parameters is reduced while minimizing the loss of encoded information. Given a learned parameter matrix W ∈ R m×n , it is formulated as finding the best rank-r approximation, as arg min W ||W − W|| F such that W = XY , where X ∈ R m×r , Y ∈ R n×r , and r min (m, n). It reduces the number of parameters from O(mn) to O((m + n)r), and its closed-form optimal solution can be found by SVD.This matrix decomposition is applicable to the FC layers and the reshaped kernels of the convolution layers. However, the natural shape of a convolution kernel is a fourth-order tensor; thus, the lowrank tensor decomposition, such as Tucker and CP decomposition (Lebedev et al., 2015; Phan et al., Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Poor. A compressive sensing approach for federated learning over massive mimo communication systems. | Published as a conference paper at ICLR 2022 FEDPARA: LOW-RANK HADAMARD PRODUCT FOR COMMUNICATION-EFFICIENT FEDERATED LEARNING |
d256194482 | Multi-view image compression plays a critical role in 3D-related applications. Existing methods adopt a predictive coding architecture, which requires joint encoding to compress the corresponding disparity as well as residual information. This demands collaboration among cameras and enforces the epipolar geometric constraint between different views, which makes it challenging to deploy these methods in distributed camera systems with randomly overlapping fields of view. Meanwhile, distributed source coding theory indicates that efficient data compression of correlated sources can be achieved by independent encoding and joint decoding, which motivates us to design a learning-based distributed multi-view image coding (LDMIC) framework. With independent encoders, LDMIC introduces a simple yet effective joint context transfer module based on the crossattention mechanism at the decoder to effectively capture the global inter-view correlations, which is insensitive to the geometric relationships between images. Experimental results show that LDMIC significantly outperforms both traditional and learning-based MIC methods while enjoying fast encoding speed. Code is released at https://github.com/Xinjie-Q/LDMIC. | Published as a conference paper at ICLR 2023 LDMIC: LEARNING-BASED DISTRIBUTED MULTI- VIEW IMAGE CODING |
d259977321 | In this work, we aim to learn dexterous manipulation of deformable objects using multi-fingered hands. Reinforcement learning approaches for dexterous rigid object manipulation would struggle in this setting due to the complexity of physics interaction with deformable objects. At the same time, previous trajectory optimization approaches with differentiable physics for deformable manipulation would suffer from local optima caused by the explosion of contact modes from hand-object interactions. To address these challenges, we propose DexDeform, a principled framework that abstracts dexterous manipulation skills from human demonstration, and refines the learned skills with differentiable physics. Concretely, we first collect a small set of human demonstrations using teleoperation. And we then train a skill model using demonstrations for planning over action abstractions in imagination. To explore the goal space, we further apply augmentations to the existing deformable shapes in demonstrations and use a gradient optimizer to refine the actions planned by the skill model. Finally, we adopt the refined trajectories as new demonstrations for finetuning the skill model. To evaluate the effectiveness of our approach, we introduce a suite of six challenging dexterous deformable object manipulation tasks. Compared with baselines, DexDeform is able to better explore and generalize across novel goals unseen in the initial human demonstrations. Additional materials can be found at our project website 1 . * Equal Contribution 1 Project website: . Learning complex dexterous manipulation with deep reinforcement learning and demonstrations. arXiv preprint arXiv: 1709.10087, 2017. 2, 4, 8, 22 Daniela Rus. In-hand dexterous manipulation of piecewise-smooth 3-d objects. | Published as a conference paper at ICLR 2023 DEXDEFORM: DEXTEROUS DEFORMABLE OBJECT MANIPULATION WITH HUMAN DEMONSTRATIONS AND DIFFERENTIABLE PHYSICS |
d198953497 | In multiagent systems (MASs), each agent makes individual decisions but all of them contribute globally to the system evolution. Learning in MASs is difficult since each agent's selection of actions must take place in the presence of other co-learning agents. Moreover, the environmental stochasticity and uncertainties increase exponentially with the increase in the number of agents. Previous works borrow various multiagent coordination mechanisms into deep learning architecture to facilitate multiagent coordination. However, none of them explicitly consider action semantics between agents that different actions have different influence on other agents. In this paper, we propose a novel network architecture, named Action Semantics Network (ASN), that explicitly represents such action semantics between agents. ASN characterizes different actions' influence on other agents using neural networks based on the action semantics between them. ASN can be easily combined with existing deep reinforcement learning (DRL) algorithms to boost their performance. Experimental results on StarCraft II micromanagement and Neural MMO show ASN significantly improves the performance of state-of-the-art DRL approaches compared with several network architectures. van den Driessche, Thore Graepel, and Demis Hassabis. Mastering the game of go without human knowledge. Nature, 550(7676):354, 2017.Amanpreet Singh, Tushar Jain, and Sainbayar Sukhbaatar. Individualized controlled continuous communication model for multiagent cooperative and competitive tasks. In | Published as a conference paper at ICLR 2020 ACTION SEMANTICS NETWORK: CONSIDERING THE EFFECTS OF ACTIONS IN MULTIAGENT SYSTEMS |
d256416230 | Generating photo-realistic video portrait with arbitrary speech audio is a crucial problem in film-making and virtual reality. Recently, several works explore the usage of neural radiance field in this task to improve 3D realness and image fidelity. However, the generalizability of previous NeRF-based methods to out-of-domain audio is limited by the small scale of training data. In this work, we propose Gene-Face, a generalized and high-fidelity NeRF-based talking face generation method, which can generate natural results corresponding to various out-of-domain audio. Specifically, we learn a variaitional motion generator on a large lip-reading corpus, and introduce a domain adaptative post-net to calibrate the result. Moreover, we learn a NeRF-based renderer conditioned on the predicted facial motion. A head-aware torso-NeRF is proposed to eliminate the head-torso separation problem. Extensive experiments show that our method achieves more generalized and high-fidelity talking face generation compared to previous methods 1 . * Authors contribute equally to this work. † Corresponding author 1 Video samples and source code are available at https://geneface.github.io arXiv:2301.13430v1 [cs.CV] 31 Jan 2023Published as a conference paper at ICLR 2023 audio with several potential outputs, it tends to generate an image with a half-opened and blurry mouth, which leads to unsatisfying image quality and bad lip-synchronization. To summarize, the current NeRF-based methods are challenged with the weak generalizability problem due to the lack of audio-to-motion training data and the "mean face" results due to the one-to-many mapping.In this work, we develop a talking face generation system called GeneFace to address these two challenges. To handle the weak generalizability problem, we devise an audio-to-motion model to predict the 3D facial landmark given the input audio. We utilize hundreds of hours of audio-motion pairs from a large-scale lip reading datasetAfouras et al.(2018)to learn a robust mapping. As for the "mean face" problem, instead of using the regression-based model, we adopt a variational autoencoder (VAE) with a flow-based prior as the architecture of the audio-to-motion model, which helps generate accurate and expressive facial motions. However, due to the domain shift between the generated landmarks (in the multi-speaker domain) and the training set of NeRF (in the target person domain), we found that the NeRF-based renderer fails to generate high-fidelity frames given the predicted landmarks. Therefore, a domain adaptation process is proposed to rig the predicted landmarks into the target person's distribution. To summarize, our system consists of three stages: | Published as a conference paper at ICLR 2023 GENEFACE: GENERALIZED AND HIGH-FIDELITY AUDIO-DRIVEN 3D TALKING FACE SYNTHESIS |
d212628243 | Active inference is a theory that underpins the way biological agent's perceive and act in the real world. At its core, active inference is based on the principle that the brain is an approximate Bayesian inference engine, building an internal generative model to drive agents towards minimal surprise. Although this theory has shown interesting results with grounding in cognitive neuroscience, its application remains limited to simulations with small, predefined sensor and state spaces. In this paper, we leverage recent advances in deep learning to build more complex generative models that can work without a predefined states space. State representations are learned end-to-end from real-world, high-dimensional sensory data such as camera frames. We also show that these generative models can be used to engage in active inference. To the best of our knowledge this is the first application of deep active inference for a real-world robot navigation task. | DEEP ACTIVE INFERENCE FOR AUTONOMOUS ROBOT NAVIGATION |
d252907411 | DescriptionSitting at the edge of the bed and facing the couch. Question q : Can I go straight to the coffee table in front of me? Scene context : 3D scan, egocentric video, birdeye view (BEV) picture, etc. Answer : No Location (optional): t t+1ABSTRACT We propose a new task to benchmark scene understanding of embodied agents: Situated Question Answering in 3D Scenes (SQA3D). Given a scene context (e.g., 3D scan), SQA3D requires the tested agent to first understand its situation (position, orientation, etc.) in the 3D scene as described by text, then reason about its surrounding environment and answer a question under that situation. Based upon 650 scenes from ScanNet, we provide a dataset centered around 6.8k unique situations, along with 20.4k descriptions and 33.4k diverse reasoning questions for these situations. These questions examine a wide spectrum of reasoning capabilities for an intelligent agent, ranging from spatial relation comprehension to commonsense understanding, navigation, and multi-hop reasoning. SQA3D imposes a significant challenge to current multi-modal especially 3D reasoning models. We evaluate various state-of-the-art approaches and find that the best one only achieves an overall score of 47.20%, while amateur human participants can reach 90.06%. We believe SQA3D could facilitate future embodied AI research with stronger situation understanding and reasoning capabilities. Code and data are released at sqa3d.github.io. | Published as a conference paper at ICLR 2023 SQA3D: SITUATED QUESTION ANSWERING IN 3D SCENES |
d222133323 | Neural networks (NNs) whose subnetworks implement reusable functions are expected to offer numerous advantages, including compositionality through efficient recombination of functional building blocks, interpretability, preventing catastrophic interference, etc. Understanding if and how NNs are modular could provide insights into how to improve them. Current inspection methods, however, fail to link modules to their functionality. In this paper, we present a novel method based on learning binary weight masks to identify individual weights and subnets responsible for specific functions. Using this powerful tool, we contribute an extensive study of emerging modularity in NNs that covers several standard architectures and datasets. We demonstrate how common NNs fail to reuse submodules and offer new insights into the related issue of systematic generalization on language tasks. 1 In general we find that li concentrates at either 0 or 1 and so thresholding is safe (see alsoFig. 7). | arxiv pre-print ARE NEURAL NETS MODULAR? INSPECTING FUNC- TIONAL MODULARITY THROUGH DIFFERENTIABLE WEIGHT MASKS |
d593434 | This paper introduces EXMOVES, learned exemplar-based features for efficient recognition of actions in videos. The entries in our descriptor are produced by evaluating a set of movement classifiers over spatial-temporal volumes of the input sequence. Each movement classifier is a simple exemplar-SVM trained on low-level features, i.e., an SVM learned using a single annotated positive space-time volume and a large number of unannotated videos.Our representation offers two main advantages. First, since our mid-level features are learned from individual video exemplars, they require minimal amount of supervision. Second, we show that simple linear classification models trained on our global video descriptor yield action recognition accuracy approaching the stateof-the-art but at orders of magnitude lower cost, since at test-time no sliding window is necessary and linear models are efficient to train and test. This enables scalable action recognition, i.e., efficient classification of a large number of actions even in massive video databases. We show the generality of our approach by building our mid-level descriptors from two different low-level feature vectors. The accuracy and efficiency of the approach are demonstrated on several large-scale action recognition benchmarks. | EXMOVES: Classifier-based Features for Scalable Action Recognition |
d220363877 | Training neural networks with auxiliary tasks is a common practice for improving the performance on a main task of interest. Two main challenges arise in this multi-task learning setting: (i) designing useful auxiliary tasks; and (ii) combining auxiliary tasks into a single coherent loss. Here, we propose a novel framework, AuxiLearn, that targets both challenges based on implicit differentiation. First, when useful auxiliaries are known, we propose learning a network that combines all losses into a single coherent objective function. This network can learn nonlinear interactions between tasks. Second, when no useful auxiliary task is known, we describe how to learn a network that generates a meaningful, novel auxiliary task. We evaluate AuxiLearn in a series of tasks and domains, including image segmentation and learning with attributes in the low data regime, and find that it consistently outperforms competing methods. * Equal contributor † Equal contributor | Published as a conference paper at ICLR 2021 AUXILIARY LEARNING BY IMPLICIT DIFFERENTIATION |
d233169075 | Humans can quickly adapt to new partners in collaborative tasks (e.g. playing basketball), because they understand which fundamental skills of the task (e.g. how to dribble, how to shoot) carry over across new partners. Humans can also quickly adapt to similar tasks with the same partners by carrying over conventions that they have developed (e.g. raising hand signals pass the ball), without learning to coordinate from scratch. To collaborate seamlessly with humans, AI agents should adapt quickly to new partners and new tasks as well. However, current approaches have not attempted to distinguish between the complexities intrinsic to a task and the conventions used by a partner, and more generally there has been little focus on leveraging conventions for adapting to new settings. In this work, we propose a learning framework that teases apart rule-dependent representation from convention-dependent representation in a principled way. We show that, under some assumptions, our rule-dependent representation is a sufficient statistic of the distribution over best-response strategies across partners. Using this separation of representations, our agents are able to adapt quickly to new partners, and to coordinate with old partners on new tasks in a zero-shot manner. We experimentally validate our approach on three collaborative tasks varying in complexity: a contextual multi-armed bandit, a block placing task, and the card game Hanabi. | Published as a conference paper at ICLR 2021 ON THE CRITICAL ROLE OF CONVENTIONS IN ADAPTIVE HUMAN-AI COLLABORATION |
d13574093 | We propose a new neurally-inspired model that can learn to encode global relationship context of visual events across time and space and to use the contextual information to modulate the analysis by synthesis process in a predictive coding framework.The model is based on the principle of mutual predictability.It learns latent contextual representations by maximizing the predictability of visual events based on local and global context information.The model can therefore interpolate missing events or predict future events in image sequences.The contextual representations modulate the prediction synthesis process by adaptively rescaling the contribution of each neuron's basis function.In contrast to standard predictive coding models, the prediction error in this model is used to update the context representation but does not alter the feedforward input for the next layer, thus is more consistent with neuro-physiological observations.We establish the computational feasibility of this model by demonstrating its ability to simultaneously infer context, as well as interpolate and predict input image sequences in a unified framework. | Predictive Encoding of Contextual Relationships for Perceptual Inference, Interpolation and Prediction |
d159321124 | Legal Friction of State Civil Apparatus Neutrality in Indonesia | |
d53474174 | The impressive lifelong learning in animal brains is primarily enabled by plastic changes in synaptic connectivity. Importantly, these changes are not passive, but are actively controlled by neuromodulation, which is itself under the control of the brain. The resulting self-modifying abilities of the brain play an important role in learning and adaptation, and are a major basis for biological reinforcement learning. Here we show for the first time that artificial neural networks with such neuromodulated plasticity can be trained with gradient descent. Extending previous work on differentiable Hebbian plasticity, we propose a differentiable formulation for the neuromodulation of plasticity. We show that neuromodulated plasticity improves the performance of neural networks on both reinforcement learning and supervised learning tasks. In one task, neuromodulated plastic LSTMs with millions of parameters outperform standard LSTMs on a benchmark language modeling task (controlling for the number of parameters). We conclude that differentiable neuromodulation of plasticity offers a powerful new framework for training neural networks. | BACKPROPAMINE: TRAINING SELF-MODIFYING NEU- RAL NETWORKS WITH DIFFERENTIABLE NEUROMODU- LATED PLASTICITY |
d1136006 | Distance metric learning (DML) approaches learn a transformation to a representation space where distance is in correspondence with a predefined notion of similarity.While such models offer a number of compelling benefits, it has been difficult for these to compete with modern classification algorithms in performance and even in feature extraction.In this work, we propose a novel approach explicitly designed to address a number of subtle yet important issues which have stymied earlier DML algorithms.It maintains an explicit model of the distributions of the different classes in representation space.It then employs this knowledge to adaptively assess similarity, and achieve local discrimination by penalizing class distribution overlap.We demonstrate the effectiveness of this idea on several tasks.Our approach achieves state-of-the-art classification results on a number of fine-grained visual recognition datasets, surpassing the standard softmax classifier and outperforming triplet loss by a relative margin of 30-40%.In terms of computational performance, it alleviates training inefficiencies in the traditional triplet loss, reaching the same error in 5-30 times fewer iterations.Beyond classification, we further validate the saliency of the learnt representations via their attribute concentration and hierarchy recovery properties, achieving 10-25% relative gains on the softmax classifier and 25-50% on triplet loss in these tasks. | METRIC LEARNING WITH ADAPTIVE DENSITY DISCRIMINATION |
d219179868 | Advanced data augmentation strategies have widely been studied to improve the generalization ability of deep learning models. Regional dropout is one of the popular solutions that guides the model to focus on less discriminative parts by randomly removing image regions, resulting in improved regularization. However, such information removal is undesirable. On the other hand, recent strategies suggest to randomly cut and mix patches and their labels among training images, to enjoy the advantages of regional dropout without having any pointless pixel in the augmented images. We argue that such random selection strategies of the patches may not necessarily represent sufficient information about the corresponding object and thereby mixing the labels according to that uninformative patch enables the model to learn unexpected feature representation. Therefore, we propose SaliencyMix that carefully selects a representative image patch with the help of a saliency map and mixes this indicative patch with the target image, thus leading the model to learn more appropriate feature representation. SaliencyMix achieves the best known top-1 error of 21.26% and 20.09% for ResNet-50 and ResNet-101 architectures on ImageNet classification, respectively, and also improves the model robustness against adversarial perturbations. Furthermore, models that are trained with SaliencyMix help to improve the object detection performance. Source code is available at https://github. | Published as a conference paper at ICLR 2021 SALIENCYMIX: A SALIENCY GUIDED DATA AUG- MENTATION STRATEGY FOR BETTER REGULARIZA- TION |
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