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d259298735 | Repeated parameter sharing in federated learning causes significant information leakage about private data, thus defeating its main purpose: data privacy. Mitigating the risk of this information leakage, using state of the art differentially private algorithms, also does not come for free. Randomized mechanisms can prevent convergence of models on learning even the useful representation functions, especially if there is more disagreement between local models on the classification functions (due to data heterogeneity). In this paper, we consider a representation federated learning objective that encourages various parties to collaboratively refine the consensus part of the model, with differential privacy guarantees, while separately allowing sufficient freedom for local personalization (without releasing it). We prove that in the linear representation setting, while the objective is non-convex, our proposed new algorithm CENTAUR converges to a ball centered around the global optimal solution at a linear rate, and the radius of the ball is proportional to the reciprocal of the privacy budget. With this novel utility analysis, we improve the SOTA utility-privacy trade-off for this problem by a factor of √ d, where d is the input dimension. We empirically evaluate our method with the image classification task on CIFAR10, CIFAR100, and EMNIST, and observe a significant performance improvement over the prior work under the same small privacy budget. The code can be found in this link. | Published as a conference paper at ICLR 2023 SHARE YOUR REPRESENTATION ONLY: GUARANTEED IMPROVEMENT OF THE PRIVACY-UTILITY TRADEOFF IN FEDERATED LEARNING |
d222125298 | We provide a general self-attention formulation to impose group equivariance to arbitrary symmetry groups. This is achieved by defining positional encodings that are invariant to the action of the group considered. Since the group acts on the positional encoding directly, group equivariant self-attention networks (GSA-Nets) are steerable by nature. Our experiments on vision benchmarks demonstrate consistent improvements of GSA-Nets over non-equivariant self-attention networks. arXiv:2010.00977v1 [cs.CV] 2 Oct 2020 ArXiv preprint. Under review. Figure 1: Behaviour of feature representations in group self-attention networks. An input rotation induces a rotation plus a cyclic permutation to the intermediary feature representations of the network.Additional examples for all the groups used in this work as well as their usage are provided in repo/demo/. Shlens. Stand-alone self-attention in vision models. arXiv preprint arXiv:1906.05909, 2019. | ArXiv preprint. Under review. GROUP EQUIVARIANT STAND-ALONE SELF-ATTENTION FOR VISION |
d256105320 | The vast amount of health data has been continuously collected for each patient, providing opportunities to support diverse healthcare predictive tasks such as seizure detection and hospitalization prediction. Existing models are mostly trained on other patients' data and evaluated on new patients. Many of them might suffer from poor generalizability. One key reason can be overfitting due to the unique information related to patient identities and their data collection environments, referred to as patient covariates in the paper. These patient covariates usually do not contribute to predicting the targets but are often difficult to remove. As a result, they can bias the model training process and impede generalization. In healthcare applications, most existing domain generalization methods assume a small number of domains. In this paper, considering the diversity of patient covariates, we propose a new setting by treating each patient as a separate domain (leading to many domains). We develop a new domain generalization method ManyDG 1 , that can scale to such many-domain problems. Our method identifies the patient domain covariates by mutual reconstruction, and removes them via an orthogonal projection step. Extensive experiments show that ManyDG can boost the generalization performance on multiple real-world healthcare tasks (e.g., 3.7% Jaccard improvements on MIMIC drug recommendation) and support realistic but challenging settings such as insufficient data and continuous learning.Published as a conference paper at ICLR 2023 insomnia (Rémi et al., 2019) could have more awake stages than ordinary people, and elders tend to have fewer rapid eye movement (REM) stages than teenagers (Ohayon et al., 2004). Furthermore, these patient covariates can be even more harmful when dealing with insufficient training data. | MA N YDG: MANY-DOMAIN GENERALIZATION FOR HEALTHCARE APPLICATIONS |
d232233677 | The study of adversarial examples and their activation has attracted significant attention for secure and robust learning with deep neural networks (DNNs). Different from existing works, in this paper, we highlight two new characteristics of adversarial examples from the channel-wise activation perspective: 1) the activation magnitudes of adversarial examples are higher than that of natural examples; and 2) the channels are activated more uniformly by adversarial examples than natural examples. We find that the state-of-the-art defense adversarial training has addressed the first issue of high activation magnitudes via training on adversarial examples, while the second issue of uniform activation remains. This motivates us to suppress redundant activation from being activated by adversarial perturbations via a Channel-wise Activation Suppressing (CAS) strategy. We show that CAS can train a model that inherently suppresses adversarial activation, and can be easily applied to existing defense methods to further improve their robustness. Our work provides a simple but generic training strategy for robustifying the intermediate layer activation of DNNs. Code is available at | Published as a conference paper at ICLR 2021 IMPROVING ADVERSARIAL ROBUSTNESS VIA CHANNEL-WISE ACTIVATION SUPPRESSING |
d238634169 | The power of a generalization system follows directly from its biases"(Mitchell 1980). Today, CNNs are incredibly powerful generalisation systems-but to what degree have we understood how their inductive bias influences model decisions?We here attempt to disentangle the various aspects that determine how a model decides. In particular, we ask: what makes one model decide differently from another? In a meticulously controlled setting, we find that (1.) irrespective of the network architecture or objective (e.g. self-supervised, semi-supervised, vision transformers, recurrent models) all models end up making similar decisions. (2.) To understand these findings, we analysed model decisions on the ImageNet validation set from epoch to epoch and image by image. We find that the ImageNet validation set, among others, suffers from dichotomous data difficulty (DDD): For the range of investigated models and their accuracies, it is dominated by 46.0% "trivial" and 11.5% "impossible" images (beyond label errors). Only 42.5% of the images could possibly be responsible for the differences between two models' decision boundaries. (3.) Only removing the "impossible" and "trivial" images allows us to see pronounced differences between models. (4.) Humans are highly accurate at predicting which images are "trivial" and "impossible" for CNNs (81.4%). This implies that in future comparisons of brains, machines and behaviour, much may be gained from investigating the decisive role of images and the distribution of their difficulties. * joint first authors in alphabetical order; + corresponding author arXiv:2110.05922v3 [cs.CV] 27 Apr 2022Published as a conference paper at ICLR 2022 (a) ResNet-18 variants (b) State-of-the-art models Figure 2: Dichotomous Data Difficulty (DDD) in a nutshell: Irrespective of model differences (e.g.architecture, hyperparameters, optimizer), most ImageNet validation images are either "trivial" (in the sense that all models classify them correctly) or "impossible" (all models make an error). This dichotomous difficulty masks underlying differences between models (as we will show later), and it affects the majority of the ImageNet dataset-i.e. not only images with label errors (red) as identified by the cleanlab package (Northcutt et al., 2021a). For comparison, a binomial distribution of errors is shown in green: this is the distribution of errors expected for completely independent models if all images were equally difficult.Published as a conference paper at ICLR 2022 Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016. Curtis Northcutt, Lu Jiang, and Isaac Chuang. Confident learning: Estimating uncertainty in dataset labels. . What shapes feature representations? exploring datasets, architectures, and training. arXiv preprint arXiv:2006.12433, 2020.Thao Nguyen, Maithra Raghu, and Simon Kornblith. Do wide and deep networks learn the same things? uncovering how neural network representations vary with width and depth. arXiv preprint arXiv:. On the surprising similarities between supervised and self-supervised models. arXiv preprint arXiv:2010.08377, 2020b. , et al. ImageNet large scale visual recognition challenge. van den Oord. Are we done with ImageNet? arXiv preprint arXiv:2006.07159, 2020. | TRIVIAL OR IMPOSSIBLE-DICHOTOMOUS DATA DIFFICULTY MASKS MODEL DIFFERENCES (ON IMAGENET AND BEYOND) |
d13022595 | Experience replay lets online reinforcement learning agents remember and reuse experiences from the past. In prior work, experience transitions were uniformly sampled from a replay memory. However, this approach simply replays transitions at the same frequency that they were originally experienced, regardless of their significance. In this paper we develop a framework for prioritizing experience, so as to replay important transitions more frequently, and therefore learn more efficiently. We use prioritized experience replay in Deep Q-Networks (DQN), a reinforcement learning algorithm that achieved human-level performance across many Atari games. DQN with prioritized experience replay achieves a new stateof-the-art, outperforming DQN with uniform replay on 41 out of 49 games. | Published as a conference paper at ICLR 2016 PRIORITIZED EXPERIENCE REPLAY |
d10465751 | Sentiment analysis predicts the presence of positive or negative emotions in a text document. In this paper, we consider higher dimensional extensions of the sentiment concept, which represent a richer set of human emotions. Our approach goes beyond previous work in that our model contains a continuous manifold rather than a finite set of human emotions. We investigate the resulting model, compare it to psychological observations, and explore its predictive capabilities. | The Manifold of Human Emotions |
d57573823 | Variational Autoencoder (VAE), a simple and effective deep generative model, has led to a number of impressive empirical successes and spawned many advanced variants and theoretical investigations. However, recent studies demonstrate that, when equipped with expressive generative distributions (aka. decoders), VAE suffers from learning uninformative latent representations with the observation called KL Varnishing, in which case VAE collapses into an unconditional generative model. In this work, we introduce mutual posterior-divergence regularization, a novel regularization that is able to control the geometry of the latent space to accomplish meaningful representation learning, while achieving comparable or superior capability of density estimation. Experiments on three image benchmark datasets demonstrate that, when equipped with powerful decoders, our model performs well both on density estimation and representation learning. | MAE: MUTUAL POSTERIOR-DIVERGENCE REGULAR- IZATION FOR VARIATIONAL AUTOENCODERS |
d257496411 | In this work, we present the Bregman Alternating Projected Gradient (BAPG) method, a single-loop algorithm that offers an approximate solution to the Gromov-Wasserstein (GW) distance. We introduce a novel relaxation technique that balances accuracy and computational efficiency, albeit with some compromises in the feasibility of the coupling map. Our analysis is based on the observation that the GW problem satisfies the Luo-Tseng error bound condition, which relates to estimating the distance of a point to the critical point set of the GW problem based on the optimality residual. This observation allows us to provide an approximation bound for the distance between the fixed-point set of BAPG and the critical point set of GW. Moreover, under a mild technical assumption, we can show that BAPG converges to its fixed point set. The effectiveness of BAPG has been validated through comprehensive numerical experiments in graph alignment and partition tasks, where it outperforms existing methods in terms of both solution quality and wall-clock time.Although the GW distance has gained a lot of attention in the machine learning and data science communities, most existing algorithms for computing the GW distance are double-loop algorithms that require another iterative algorithm as a subroutine, making them not ideal for practical use. Recently, an entropy-regularized iterative sinkhorn projection algorithm called eBPG was proposed bySolomon et al. (2016), which has been proven to converge under the Kurdyka-Łojasiewicz framework. However, eBPG has several limitations. Firstly, it addresses an entropic-regularized GW objective, whose regularization parameter has a major impact on the model's performance. Secondly, it requires solving an entropic optimal transport problem at each iteration, which is both computationally expensive and not practical. In an effort to solve the GW problem directly,Xu et al. (2019b)proposed the Bregman projected gradient (BPG), which is still a double-loop algorithm that Published as a conference paper at ICLR 2023 relies on another iterative algorithm as a subroutine. Additionally, it suffers from numerical instability due to the lack of an entropic regularizer. While Vayer et al. (2019a); Mémoli (2011) introduced the Frank-Wolfe method to solve the GW problem, they still relied on linear programming solvers and line-search schemes, making it unsuitable for even medium-sized tasks. Recently, Xu et al. (2019b) developed a simple heuristic, single-loop method called BPG-S based on BPG that showed good empirical performance on node correspondence tasks. However, its performance in the presence of noise is unknown due to the lack of theoretical support.The main challenge lies in efficiently tackling the Birkhoff polytope constraints (i.e., the polytope of doubly stochastic matrices) for the coupling matrix. The key issue is that there is no closed update for its Bregman projection, which forces current algorithms to rely on computationally expensive or hyperparameter-sensitive iterative methods. To address this difficulty, we propose a single-loop algorithm (BAPG) that solves the GW distance approximately. Our solution incorporates a novel relaxation technique that sacrifices some feasibility of the coupling map to achieve computational efficiency. This violation is acceptable for certain learning tasks, such as graph alignment and partition, where the quality of the coupling is not the primary concern. We find that BAPG can obtain desirable performance on some graph learning tasks as the performance measure for those tasks is the matching accuracy instead of the sharpness of the probabilistic correspondence. In conclusion, BAPG offers a way to sacrifice the feasibility for both computational efficiency and matching accuracy.In our approach, we decouple the Birkhoff polytope constraint into separate simplex constraints for the rows and columns. The projected gradient descent is then performed on a constructed penalty function using an alternating fashion. By utilizing the closed-form Bregman projection of the simplex constraint with relative entropy as the base function, BAPG only requires matrix-vector/matrix-matrix multiplications and element-wise matrix operations at each iteration, making it a computationally efficient algorithm. Thus, BAPG has several convenient properties such as compatibility with GPU implementation, robustness with regards to the step size (the only hyperparameter), and low memory requirements.Next, we investigate the approximation bound and convergence behavior of BAPG. We surprisingly discover that the GW problem satisfies the Luo-Tseng error bound condition(Luo & Tseng, 1992). This fact allows us to bound the distance between the fixed-point set of BAPG and the critical point set of the GW problem, which is a notable departure from the usual approach of utilizing the Luo-Tseng error bound condition in establishing the linear convergence rate for structured convex problems (Zhou & So, 2017). With this finding, we are able to quantify the approximation bound for the fixed-point set of BAPG explicitly. Moreover, we establish the subsequence convergence result when the accumulative asymmetric error of the Bregman distance is bounded.Lastly, we present extensive experimental results to validate the effectiveness of BAPG for graph alignment and graph partition. Our results demonstrate that BAPG outperforms other heuristic single-loop and theoretically sound double-loop methods in terms of both computational efficiency and matching accuracy. We also conduct a sensitivity analysis of BAPG and demonstrate the benefits of its GPU acceleration through experiments on both synthetic and real-world datasets. All theoretical insights and results have been well-corroborated in the experiments.PROPOSED ALGORITHMIn this section, we begin by presenting the GW distance as a nonconvex quadratic problem with Birkhoff polytope constraints. We then delve into the theoretical insights and computational characteristics of our proposed algorithm, BAPG. | A CONVERGENT SINGLE-LOOP ALGORITHM FOR RE- LAXATION OF GROMOV-WASSERSTEIN IN GRAPH DATA |
d159181884 | Comparative Law in Asia: The Case for Intra-Asia Intensification | |
d248834505 | Aiming to find a program satisfying the user intent given input-output examples, program synthesis has attracted increasing interest in the area of machine learning. Despite the promising performance of existing methods, most of their success comes from the privileged information of well-designed input-output examples. However, providing such input-output examples is unrealistic because it requires the users to have the ability to describe the underlying program with a few inputoutput examples under the training distribution. In this work, we propose a querybased framework that trains a query neural network to generate informative inputoutput examples automatically and interactively from a large query space. The quality of the query depends on the amount of the mutual information between the query and the corresponding program, which can guide the optimization of the query framework. To estimate the mutual information more accurately, we introduce the functional space (F-space) which models the relevance between the input-output examples and the programs in a differentiable way. We evaluate the effectiveness and generalization of the proposed query-based framework on the Karel task and the list processing task. Experimental results show that the querybased framework can generate informative input-output examples which achieve and even outperform well-designed input-output examples. | Published as a conference paper at ICLR 2022 NEURAL PROGRAM SYNTHESIS WITH QUERY |
d12538994 | We consider the task of learning to extract motion from videos. To this end, we show that the detection of spatial transformations can be viewed as the detection of synchrony between the image sequence and a sequence of features undergoing the motion we wish to detect. We show that learning about synchrony is possible using very fast, local learning rules, by introducing multiplicative "gating" interactions between hidden units across frames. This makes it possible to achieve competitive performance in a wide variety of motion estimation tasks, using a small fraction of the time required to learn features, and to outperform hand-crafted spatio-temporal features by a large margin. We also show how learning about synchrony can be viewed as performing greedy parameter estimation in the well-known motion energy model. | Learning to encode motion using spatio-temporal synchrony |
d235652267 | We empirically show that the test error of deep networks can be estimated by training the same architecture on the same training set but with two different runs of Stochastic Gradient Descent (SGD), and then measuring the disagreement rate between the two networks on unlabeled test data. This builds on -and is a stronger version of -the observation in Nakkiran & Bansal (2020), which requires the runs to be on separate training sets. We further theoretically show that this peculiar phenomenon arises from the well-calibrated nature of ensembles of SGD-trained models. This finding not only provides a simple empirical measure to directly predict the test error using unlabeled test data, but also establishes a new conceptual connection between generalization and calibration. | Published as a conference paper at ICLR 2022 ASSESSING GENERALIZATION VIA DISAGREEMENT |
d237605129 | Many computer vision systems require low-cost segmentation algorithms based on deep learning, either because of the enormous size of input images or limited computational budget. Common solutions uniformly downsample the input images to meet memory constraints, assuming all pixels are equally informative. In this work, we demonstrate that this assumption can harm the segmentation performance because the segmentation difficulty varies spatially (seeFigure 1"Uniform"). We combat this problem by introducing a learnable downsampling module, which can be optimised together with the given segmentation model in an end-to-end fashion. We formulate the problem of training such downsampling module as optimisation of sampling density distributions over the input images given their low-resolution views. To defend against degenerate solutions (e.g. over-sampling trivial regions like the backgrounds), we propose a regularisation term that encourages the sampling locations to concentrate around the object boundaries. We find the downsampling module learns to sample more densely at difficult locations, thereby improving the segmentation performance (seeFigure 1"Ours"). Our experiments on benchmarks of high-resolution street view, aerial and medical images demonstrate substantial improvements in terms of efficiency-and-accuracy trade-off compared to both uniform downsampling and two recent advanced downsampling techniques. | Published as a conference paper at ICLR 2022 LEARNING TO DOWNSAMPLE FOR SEGMENTATION OF ULTRA-HIGH RESOLUTION IMAGES |
d257427479 | Existing approaches to system identification (estimating the physical parameters of an object) from videos assume known object geometries. This precludes their applicability in a vast majority of scenes where object geometries are complex or unknown. In this work, we aim to identify parameters characterizing a physical system from a set of multi-view videos without any assumption on object geometry or topology. To this end, we propose "Physics Augmented Continuum Neural Radiance Fields" (PAC-NeRF), to estimate both the unknown geometry and physical parameters of highly dynamic objects from multi-view videos. We design PAC-NeRF to only ever produce physically plausible states by enforcing the neural radiance field to follow the conservation laws of continuum mechanics. For this, we design a hybrid Eulerian-Lagrangian representation of the neural radiance field, i.e., we use the Eulerian grid representation for NeRF density and color fields, while advecting the neural radiance fields via Lagrangian particles. This hybrid Eulerian-Lagrangian representation seamlessly blends efficient neural rendering with the material point method (MPM) for robust differentiable physics simulation. We validate the effectiveness of our proposed framework on geometry and physical parameter estimation over a vast range of materials, including elastic bodies, plasticine, sand, Newtonian and non-Newtonian fluids, and demonstrate significant performance gain on most tasks 1 . * This work was done during an internship at the MIT-IBM Watson AI Lab 1 Demos are available on the project webpage: | PAC-NERF: PHYSICS AUGMENTED CONTINUUM NEURAL RADIANCE FIELDS FOR GEOMETRY- AGNOSTIC SYSTEM IDENTIFICATION |
d238634635 | The pretrain-finetune paradigm has shown outstanding performance on many applications of deep learning, where a model is pre-trained on an upstream large dataset (e.g. ImageNet), and is then fine-tuned to different downstream tasks. Though for most cases, the pre-training stage is conducted based on supervised methods, recent works on self-supervised pre-training have shown powerful transferability and even outperform supervised pre-training on multiple downstream tasks. It thus remains as an open question how to better generalize supervised pretraining model to downstream tasks. In this paper, we argue that the worse transferability of existing supervised pre-training methods arise from the negligence of valuable intra-class semantic difference. This is because these methods tend to push images from the same class close to each other despite of the large diversity in their visual contents, a problem to which referred as "overfit of upstream tasks". To alleviate this problem, we propose a new supervised pre-training method based on Leave-One-Out K-Nearest-Neighbor, or LOOK for short. It relieves the problem of overfitting upstream tasks by only requiring each image to share its class label with most of its k nearest neighbors, thus allowing each class to exhibit a multi-mode distribution and consequentially preserving part of intra-class difference for better transferring to downstream tasks. We developed efficient implementation of the proposed method that scales well to large datasets. Extensive empirical studies on multiple downstream tasks show that LOOK outperforms other state-of-the-art methods for supervised and self-supervised pre-training. | Published as a conference paper at ICLR 2022 RETHINKING SUPERVISED PRE-TRAINING FOR BETTER DOWNSTREAM TRANSFERRING |
d252815793 | The ideally disentangled latent space in GAN involves the global representation of latent space with semantic attribute coordinates. In other words, considering that this disentangled latent space is a vector space, there exists the global semantic basis where each basis component describes one attribute of generated images. In this paper, we propose an unsupervised method for finding this global semantic basis in the intermediate latent space in GANs. This semantic basis represents sample-independent meaningful perturbations that change the same semantic attribute of an image on the entire latent space. The proposed global basis, called Fréchet basis, is derived by introducing Fréchet mean to the local semantic perturbations in a latent space. Fréchet basis is discovered in two stages. First, the global semantic subspace is discovered by the Fréchet mean in the Grassmannian manifold of the local semantic subspaces. Second, Fréchet basis is found by optimizing a basis of the semantic subspace via the Fréchet mean in the Special Orthogonal Group. Experimental results demonstrate that Fréchet basis provides better semantic factorization and robustness compared to the previous methods. Moreover, we suggest the basis refinement scheme for the previous methods. The quantitative experiments show that the refined basis achieves better semantic factorization while constrained on the same semantic subspace given by the previous method. | Published as a conference paper at ICLR 2023 FINDING THE GLOBAL SEMANTIC REPRESENTATION IN GAN THROUGH FRÉCHET MEAN |
d9996719 | Motivated by the recent progress in generative models, we introduce a model that generates images from natural language descriptions. The proposed model iteratively draws patches on a canvas, while attending to the relevant words in the description. After training on Microsoft COCO, we compare our model with several baseline generative models on image generation and retrieval tasks. We demonstrate that our model produces higher quality samples than other approaches and generates images with novel scene compositions corresponding to previously unseen captions in the dataset. | Published as a conference paper at ICLR 2016 GENERATING IMAGES FROM CAPTIONS WITH ATTENTION |
d225068784 | Reinforcement learning (RL) has achieved impressive performance in a variety of online settings in which an agent's ability to query the environment for transitions and rewards is effectively unlimited.However, in many practical applications, the situation is reversed: an agent may have access to large amounts of undirected offline experience data, while access to the online environment is severely limited.In this work, we focus on this offline setting.Our main insight is that, when presented with offline data composed of a variety of behaviors, an effective way to leverage this data is to extract a continuous space of recurring and temporally extended primitive behaviors before using these primitives for downstream task learning.Primitives extracted in this way serve two purposes: they delineate the behaviors that are supported by the data from those that are not, making them useful for avoiding distributional shift in offline RL; and they provide a degree of temporal abstraction, which reduces the effective horizon yielding better learning in theory, and improved offline RL in practice.In addition to benefiting offline policy optimization, we show that performing offline primitive learning in this way can also be leveraged for improving few-shot imitation learning as well as exploration and transfer in online RL on a variety of benchmark domains.Visualizations and code are available at https://sites.google.com/view/opal-iclr | OPAL: OFFLINE PRIMITIVE DISCOVERY FOR ACCEL-ERATING OFFLINE REINFORCEMENT LEARNING |
d2181703 | Current state-of-the-art deep learning systems for visual object recognition and detection use purely supervised training with regularization such as dropout to avoid overfitting. The performance depends critically on the amount of labeled examples, and in current practice the labels are assumed to be unambiguous and accurate. However, this assumption often does not hold; e.g. in recognition, class labels may be missing; in detection, objects in the image may not be localized; and in general, the labeling may be subjective. In this work we propose a generic way to handle noisy and incomplete labeling by augmenting the prediction objective with a notion of consistency. We consider a prediction consistent if the same prediction is made given similar percepts, where the notion of similarity is between deep network features computed from the input data. In experiments we demonstrate that our approach yields substantial robustness to label noise on several datasets. On MNIST handwritten digits, we show that our model is robust to label corruption. On the Toronto Face Database, we show that our model handles well the case of subjective labels in emotion recognition, achieving state-of-theart results, and can also benefit from unlabeled face images with no modification to our method. On the ILSVRC2014 detection challenge data, we show that our approach extends to very deep networks, high resolution images and structured outputs, and results in improved scalable detection. | Under review as a conference paper at ICLR 2015 TRAINING DEEP NEURAL NETWORKS ON NOISY LABELS WITH BOOTSTRAPPING |
d7823468 | This paper introduces Grid Long Short-Term Memory, a network of LSTM cells arranged in a multidimensional grid that can be applied to vectors, sequences or higher dimensional data such as images. The network differs from existing deep LSTM architectures in that the cells are connected between network layers as well as along the spatiotemporal dimensions of the data. It therefore provides a unified way of using LSTM for both deep and sequential computation. We apply the model to algorithmic tasks such as integer addition and determining the parity of random binary vectors. It is able to solve these problems for 15-digit integers and 250-bit vectors respectively. We then give results for three empirical tasks. We find that 2D Grid LSTM achieves 1.47 bits per character on the Wikipedia character prediction benchmark, which is state-of-the-art among neural approaches. We also observe that a two-dimensional translation model based on Grid LSTM outperforms a phrase-based reference system on a Chinese-to-English translation task, and that 3D Grid LSTM yields a near state-of-the-art error rate of 0.32% on MNIST. | Grid Long Short-Term Memory |
d257771836 | Segmentation uncertainty models predict a distribution over plausible segmentations for a given input, which they learn from the annotator variation in the training set. However, in practice these annotations can differ systematically in the way they are generated, for example through the use of different labeling tools. This results in datasets that contain both data variability and differing label styles. In this paper, we demonstrate that applying state-of-the-art segmentation uncertainty models on such datasets can lead to model bias caused by the different label styles. We present an updated modelling objective conditioning on labeling style for aleatoric uncertainty estimation, and modify two state-of-the-art-architectures for segmentation uncertainty accordingly. We show with extensive experiments that this method reduces label style bias, while improving segmentation performance, increasing the applicability of segmentation uncertainty models in the wild. We curate two datasets, with annotations in different label styles, which we will make publicly available along with our code upon publication.Published as a conference paper at ICLR 2023 labeling tools. As a result, individual choices and external factors affect how annotations are made; we term this label style.Figure 1shows an example of how annotations may vary in label style.Label style can also depend on label cost: While detailed annotations are desirable, they also take more time, and one might desire to train models on cheaper, less detailed annotations. In the example ofFig. 1, we have access to both detailed and coarse, or weak, annotations. It is not clear that adding the weaker annotations will necessarily improve performance; removing them to train on fewer but higher quality annotations could also be beneficial. | Published as a conference paper at ICLR 2023 THAT LABEL'S GOT STYLE: HANDLING LABEL STYLE BIAS FOR UNCERTAIN IMAGE SEGMENTATION |
d13890001 | The increasingly photorealistic sample quality of generative image models suggests their feasibility in applications beyond image generation. We present the Neural Photo Editor, an interface that leverages the power of generative neural networks to make large, semantically coherent changes to existing images. To tackle the challenge of achieving accurate reconstructions without loss of feature quality, we introduce the Introspective Adversarial Network, a novel hybridization of the VAE and GAN. Our model efficiently captures long-range dependencies through use of a computational block based on weight-shared dilated convolutions, and improves generalization performance with Orthogonal Regularization, a novel weight regularization method. We validate our contributions on CelebA, SVHN, and CIFAR-100, and produce samples and reconstructions with high visual fidelity. | Published as a conference paper at ICLR 2017 NEURAL PHOTO EDITING WITH INTROSPECTIVE AD- VERSARIAL NETWORKS |
d249626510 | Our goal is to extend the denoising diffusion implicit model (DDIM) to general diffusion models (DMs) besides isotropic diffusions. Instead of constructing a non-Markov noising process as in the original DDIM, we examine the mechanism of DDIM from a numerical perspective. We discover that the DDIM can be obtained by using some specific approximations of the score when solving the corresponding stochastic differential equation. We present an interpretation of the accelerating effects of DDIM that also explains the advantages of a deterministic sampling scheme over the stochastic one for fast sampling. Building on this insight, we extend DDIM to general DMs, coined generalized DDIM (gDDIM), with a small but delicate modification in parameterizing the score network. We validate gDDIM in two non-isotropic DMs: Blurring diffusion model (BDM) and Critically-damped Langevin diffusion model (CLD). We observe more than 20 times acceleration in BDM. In the CLD, a diffusion model by augmenting the diffusion process with velocity, our algorithm achieves an FID score of 2.26, on CIFAR10, with only 50 number of score function evaluations (NFEs) and an FID score of 2.86 with only 27 NFEs. Project page and code: https://github.com/qshzh/gDDIM. | Published as a conference paper at ICLR 2023 GDDIM: GENERALIZED DENOISING DIFFUSION IM- PLICIT MODELS |
d85457862 | We study data-driven methods for community detection in graphs. This estimation problem is typically formulated in terms of the spectrum of certain operators, as well as via posterior inference under certain probabilistic graphical models. Focusing on random graph families such as the Stochastic Block Model, recent research has unified both approaches, and identified both statistical and computational signal-to-noise detection thresholds. We identify the resulting class of algorithms with a generic family of graph neural networks and show that they can reach those detection thresholds in a purely data-driven manner, without access to the underlying generative models and with no parameter assumptions. The resulting model is also tested on real datasets, requiring less computational steps and performing significantly better than rigid parametric models. | Community Detection with Graph Neural Networks |
d256231177 | Diffusion models have emerged as powerful generative models in the text-toimage domain. This paper studies their application as observation-to-action models for imitating human behaviour in sequential environments. Human behaviour is stochastic and multimodal, with structured correlations between action dimensions. Meanwhile, standard modelling choices in behaviour cloning are limited in their expressiveness and may introduce bias into the cloned policy. We begin by pointing out the limitations of these choices. We then propose that diffusion models are an excellent fit for imitating human behaviour, since they learn an expressive distribution over the joint action space. We introduce several innovations to make diffusion models suitable for sequential environments; designing suitable architectures, investigating the role of guidance, and developing reliable sampling strategies. Experimentally, diffusion models closely match human demonstrations in a simulated robotic control task and a modern 3D gaming environment.Published as a conference paper at ICLR 2023 MSE. A popular choice for BC in continuous action spaces approximates p(a|o) by a point-estimate that is optimised via MSE. This makes a surprisingly strong baseline in the literature despite its simplicity. However, MSE suffers from two limitations that harm its applicability to our goal of modelling the full, complex distributions of human behaviour. 1) MSE outputs a point-estimate. This precludes it from capturing any variance or multimodality present in p(a|o). 2) Due to its optimisation objective, MSE learns the 'average' of the distribution. This can bias the estimate towards more frequently occurring actions, or can even lead to out-of-distribution actions (e.g. picking the action between two modes). The first can be partially mitigated by instead assuming a Gaussian distribution, predicting a variance for each action dimension and sampling from the resulting Gaussian. However, due to the MSE objective, the learnt mean is still the average of the observed action distribution. These limitations are visualised inFigure 1.Discretised.A second popular choice is to discretise each continuous action dimension into B bins, and frame it as a classification task. This has two major limitations. 1) Quantisation errors arise since the model outputs a single value for each bin. 2) Since each action dimension is treated independently, the marginal rather than the joint distribution is learnt. This can lead to issues during sampling whereby dependencies between dimensions are ignored, leading to 'uncoordinated' behaviour. This can be observed inFigure 1where points outside of the true distribution have been sampled in the bottom-right corner. This can be remedied by modelling action dimensions autoregressively, but these models bring their own challenges and drawbacks (Lin et al., 2021).K-Means. Another method that accounts for dependencies between action dimensions, first clusters the actions across the dataset into K bins (rather than B |a| ) using K-Means. This discretises the joint-action distribution, rather than the marginal as in 'Discretised'. Each action is then associated with its nearest cluster, and learning can again be framed as a classification task. This approach Published as a conference paper at ICLR 2023 Fleet, et al. Imagen video: High definition video generation with diffusion models. arXiv preprint arXiv:2210.02303, 2022. text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:ior transformers: Cloning k modes with one stone. Advances in neural information processing systems, 2022. | Published as a conference paper at ICLR 2023 IMITATING HUMAN BEHAVIOUR WITH DIFFUSION MODELS |
d239024453 | Despite rapid advances in continual learning, a large body of research is devoted to improving performance in the existing setups. While a handful of work do propose new continual learning setups, they still lack practicality in certain aspects. For better practicality, we first propose a novel continual learning setup that is online, task-free, class-incremental, of blurry task boundaries and subject to inference queries at any moment. We additionally propose a new metric to better measure the performance of the continual learning methods subject to inference queries at any moment. To address the challenging setup and evaluation protocol, we propose an effective method that employs a new memory management scheme and novel learning techniques. Our empirical validation demonstrates that the proposed method outperforms prior arts by large margins. Code and data splits are available at https://github.com/naver-ai/i-Blurry. * indicates equal contribution. † indicates corresponding author. This work was done while HK, DK and JC were interns and an AI technical advisor at NAVER AI Lab.Published as a conference paper at ICLR 2022 models to provide good inference results at any time. To accurately evaluate whether a CL model is effective at such 'any-time' inference, we need a new metric for CL models. | ONLINE CONTINUAL LEARNING ON CLASS INCRE- MENTAL BLURRY TASK CONFIGURATION WITH ANY- TIME INFERENCE |
d4712464 | The objective of transfer reinforcement learning is to generalize from a set of previous tasks to unseen new tasks. In this work, we focus on the transfer scenario where the dynamics among tasks are the same, but their goals differ. Although general value function(Sutton et al., 2011)has been shown to be useful for knowledge transfer, learning a universal value function can be challenging in practice.To attack this, we propose (1) to use universal successor representations (USR) to represent the transferable knowledge and (2) a USR approximator (USRA) that can be trained by interacting with the environment. Our experiments show that USR can be effectively applied to new tasks, and the agent initialized by the trained USRA can achieve the goal considerably faster than random initialization.Matthew E Taylor and Peter Stone. Transfer learning for reinforcement learning domains: A survey. | Workshop track -ICLR 2018 UNIVERSAL SUCCESSOR REPRESENTATIONS FOR TRANSFER REINFORCEMENT LEARNING |
d260499190 | We consider the natural problem of learning a ReLU network from queries, which was recently remotivated by model extraction attacks. In this work, we present a polynomial-time algorithm that can learn a depth-two ReLU network from queries under mild general position assumptions. We also present a polynomial-time algorithm that, under mild general position assumptions, can learn a rich class of depth-three ReLU networks from queries. For instance, it can learn most networks where the number of first layer neurons is smaller than the dimension and the number of second layer neurons. These two results substantially improve state-of-the-art: Until our work, polynomial-time algorithms were only shown to learn from queries depth-two networks under the assumption that either the underlying distribution is Gaussian (Chen et al.(2021)) or that the weights matrix rows are linearly independent (Milli et al.(2019)). For depth three or more, there were no known poly-time results.Published as a conference paper at ICLR 2023 2. A polynomial time and a query complexity algorithm for exact reconstruction of a threelayer neural network under mild general position assumptions, with the additional assumptions that the number of first layer neurons is smaller than the input dimension and the assumption that the second layer has non-zero partial derivatives. The last assumption is valid for most networks with more second layer neurons than first layer neurons.The mild general position assumptions are further explained in section 3.3. However, we note that the proposed algorithm will work on any two-layer neural network except for a set with a zero Lebesgue measure. Furthermore, it will work in polynomial time provided that the input weights are slightly perturbed (for instance, each weight is perturbed by adding a uniform number in [−2 −d , 2 −d ]) At a very high level, the basis of our approach is to find points in which the linearity of the network breaks and extract neurons by recovering the affine transformations computed by the network near these points. This approach was taken by the previous theoretical papers Milli et al. (2019); Chen et al. (2021) and also in the empirical works of Carlini et al. (2020); Jagielski et al. (2019). In order to derive our results, we add several ideas to the existing techniques, including the ability to distinguish first from second layer neurons, which allows us to deal with three-layer networks, as well as the ability to reconstruct the neurons correctly in general depth-two networks with any finite width in a polynomial time, without assuming that the rows are independent. | Published as a conference paper at ICLR 2023 AN EXACT POLY-TIME MEMBERSHIP-QUERIES AL- GORITHM FOR EXTRACTING A THREE-LAYER RELU NETWORK |
d246996668 | Few-shot learning is an established topic in natural images for years, but few work is attended to histology images, which is of high clinical value since well-labeled datasets and rare abnormal samples are expensive to collect. Here, we facilitate the study of few-shot learning in histology images by setting up three cross-domain tasks that simulate real clinics problems. To enable label-efficient learning and better generalizability, we propose to incorporate contrastive learning (CL) with latent augmentation (LA) to build a few-shot system. CL learns useful representations without manual labels, while LA transfers semantic variations of the base dataset in an unsupervised way. These two components fully exploit unlabeled training data and can scale gracefully to other label-hungry problems. In experiments, we find i) models learned by CL generalize better than supervised learning for histology images in unseen classes, and ii) LA brings consistent gains over baselines. Prior studies of self-supervised learning mainly focus on ImageNet-like images, which only present a dominant object in their centers. Recent attention has been paid to images with multi-objects and multi-textures (Chen & Li, 2020). Histology images are a natural choice for such a study. We show the superiority of CL over supervised learning in terms of generalization for such data and provide our empirical understanding for this observation. The findings in this work could contribute to understanding how the model generalizes in the context of both representation learning and histological image analysis. Code is available at https://github.com/TencentAILabHealthcare/Few-shot-WSI. Hertz. A simple weight decay can improve generalization. In Advances in neural information processing systems, pp. 950-957, 1992. -dermdiagnosis: few-shot skin disease identification using meta-learning. In | Published as a conference paper at ICLR 2022 TOWARDS BETTER UNDERSTANDING AND BETTER GENERALIZATION OF FEW-SHOT CLASSIFICATION IN HISTOLOGY IMAGES WITH CONTRASTIVE LEARNING |
d225039984 | Trajectory prediction is a critical part of many AI applications, for example, the safe operation of autonomous vehicles. However, current methods are prone to making inconsistent and physically unrealistic predictions. We leverage insights from fluid dynamics to overcome this limitation by considering internal symmetry in trajectories. We propose a novel model, Equivariant Continous COnvolution (ECCO) for improved trajectory prediction. ECCO uses rotationally-equivariant continuous convolutions to embed the symmetries of the system. On two realworld vehicle and pedestrian trajectory datasets, ECCO attains competitive accuracy with significantly fewer parameters. It is also more sample efficient, generalizing automatically from few data points in any orientation. Lastly, ECCO improves generalization with equivariance, resulting in more physically consistent predictions. Our method provides a fresh perspective towards increasing trust and transparency in deep learning models. * Equal Contribution arXiv:2010.11344v1 [cs.LG] 21 Oct 2020Preprint. Under review. model must understand the physical behavior of vehicles together with human psychology. It should distinguish left from right turns, and give consistent outputs for intersections rotated with different orientation. As shown inFigure 1, a driver's velocity rotates with the entire scene, whereas vehicle interactions are invariant to such a rotation. Likewise, psychological factors such as reaction speed or attention may be considered vectors with prescribed transformation properties.In this paper, we propose an equivariant continuous convolutional model, ECCO, for trajectory forecasting. Continuous convolution generalizes discrete convolution and is adapted to data in manyparticle systems with complex local interactions. Ummenhofer et al. (2019) designed a model using continuous convolutions for particle-based fluid simulations. Meanwhile, equivariance to group symmetries has proven to be a powerful tool to integrate physical intuition in physical science applications(Wang et al., 2020;Brown & Lunter, 2019;Kanwar et al., 2020). Here, we test the hypothesis that an equivariant model can also capture internal symmetry in non-physical human behavior. Our model utilizes a novel weight sharing scheme, torus kernels, and is rotationally equivariant. We evaluate our model on two real-world trajectory datasets: Argoverse autonomous vehicle dataset(Chang et al., 2019)and TrajNet++ pedestrian trajectory forecasting challenge(Kothari et al., 2020). We demonstrate on par or better prediction accuracy to baseline models with fewer parameters, better sample efficiency, and stronger generalization properties. Lastly, we demonstrate theoretically and experimentally that our polar coordinate-indexed filters have lower equivariance discretization error due to being better adapted to the symmetry group.Our main contributions are as follows:• We propose Equivariant Continous COnvolution (ECCO), a rotationally equivariant deep neural network that can capture internal symmetry in trajectories.• We design ECCO using a novel weight sharing scheme based on orbit decomposition and polar coordinate-indexed filters. We implement equivariance for both the standard and regular representation L 2 (SO(2)).• On benchmark Argoverse and TrajNet++ datasets, ECCO demonstrates comparable accuracy while enjoying better generalization, fewer parameters, and better sample complexity. | Preprint. Under review. TRAJECTORY PREDICTION USING EQUIVARIANT CON- TINUOUS CONVOLUTION |
d67855770 | The recent direction of unpaired image-to-image translation is on one hand very exciting as it alleviates the big burden in obtaining label-intensive pixel-to-pixel supervision, but it is on the other hand not fully satisfactory due to the presence of artifacts and degenerated transformations. In this paper, we take a manifold view of the problem by introducing a smoothness term over the sample graph to attain harmonic functions to enforce consistent mappings during the translation. We develop HarmonicGAN to learn bi-directional translations between the source and the target domains. With the help of similarity-consistency, the inherent selfconsistency property of samples can be maintained. Distance metrics defined on two types of features including histogram and CNN are exploited. Under an identical problem setting as CycleGAN, without additional manual inputs and only at a small training-time cost, HarmonicGAN demonstrates a significant qualitative and quantitative improvement over the state of the art, as well as improved interpretability. We show experimental results in a number of applications including medical imaging, object transfiguration, and semantic labeling. We outperform the competing methods in all tasks, and for a medical imaging task in particular our method turns CycleGAN from a failure to a success, halving the mean-squared error, and generating images that radiologists prefer over competing methods in 95% of cases. | HARMONIC UNPAIRED IMAGE-TO-IMAGE TRANSLA- TION |
d212718244 | We consider training machine learning models that are fair in the sense that their performance is invariant under certain sensitive perturbations to the inputs. For example, the performance of a resume screening system should be invariant under changes to the gender and/or ethnicity of the applicant. We formalize this notion of algorithmic fairness as a variant of individual fairness and develop a distributionally robust optimization approach to enforce it during training. We also demonstrate the effectiveness of the approach on two ML tasks that are susceptible to gender and racial biases. | Published as a conference paper at ICLR 2020 TRAINING INDIVIDUALLY FAIR ML MODELS WITH SENSITIVE SUBSPACE ROBUSTNESS |
d15454326 | Why Size Matters: Feature Coding as Nyström Sampling | |
d246473016 | Recent work suggests that feature constraints in the training datasets of deep neural networks (DNNs) drive robustness to adversarial noise . The representations learned by such adversarially robust networks have also been shown to be more human perceptually-aligned than non-robust networks via image manipulations . Despite appearing closer to human visual perception, it is unclear if the constraints in robust DNN representations match biological constraints found in human vision. Human vision seems to rely on texture-based/summary statistic representations in the periphery, which have been shown to explain phenomena such as crowding(Balas et al., 2009)and performance on visual search tasks (Rosenholtz et al., 2012). To understand how adversarially robust optimizations/representations compare to human vision, we performed a psychophysics experiment using a metamer task similar to Freeman & Simoncelli (2011); Wallis et al. (2019); Deza et al. (2019b)where we evaluated how well human observers could distinguish between images synthesized to match adversarially robust representations compared to nonrobust representations and a texture synthesis model of peripheral vision (Texforms (Long et al., 2018)). We found that the discriminability of robust representation and texture model images decreased to near chance performance as stimuli were presented farther in the periphery. Moreover, performance on robust and texture-model images showed similar trends within participants, while performance on non-robust representations changed minimally across the visual field. These results together suggest that (1) adversarially robust representations capture peripheral computation better than non-robust representations and (2) robust representations capture peripheral computation similar to current state-of-the-art texture peripheral vision models. More broadly, our findings support the idea that localized texture summary statistic representations may drive human invariance to adversarial perturbations and that the incorporation of such representations in DNNs could give rise to useful properties like adversarial robustness. Link to Code/Data: https://github.com/anneharrington/Adversarially-Robust-Periphery. | Published as a conference paper at ICLR 2022 FINDING BIOLOGICAL PLAUSIBILITY FOR ADVER- SARIALLY ROBUST FEATURES VIA METAMERIC TASKS |
d246035679 | Arguably the most fundamental question in the theory of generative adversarial networks (GANs) is to understand to what extent GANs can actually learn the underlying distribution. Theoretical and empirical evidence (see e.g. [ARZ18]) suggests local optimality of the empirical training objective is insufficient. Yet, it does not rule out the possibility that achieving a true population minimax optimal solution might imply distribution learning.In this paper, we show that standard cryptographic assumptions imply that this stronger condition is still insufficient. Namely, we show that if local pseudorandom generators (PRGs) exist, then for a large family of natural continuous target distributions, there are ReLU network generators of constant depth and polynomial size which take Gaussian random seeds so that (i) the output is far in Wasserstein distance from the target distribution, but (ii) no polynomially large Lipschitz discriminator ReLU network can detect this. This implies that even achieving a population minimax optimal solution to the Wasserstein GAN objective is likely insufficient for distribution learning in the usual statistical sense. Our techniques reveal a deep connection between GANs and PRGs, which we believe will lead to further insights into the computational landscape of GANs. | Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs * |
d256662429 | The canonical formulation of federated learning treats it as a distributed optimization problem where the model parameters are optimized against a global loss function that decomposes across client loss functions. A recent alternative formulation instead treats federated learning as a distributed inference problem, where the goal is to infer a global posterior from partitioned client data(Al-Shedivat et al., 2021). This paper extends the inference view and describes a variational inference formulation of federated learning where the goal is to find a global variational posterior that well-approximates the true posterior. This naturally motivates an expectation propagation approach to federated learning (FedEP), where approximations to the global posterior are iteratively refined through probabilistic message-passing between the central server and the clients. We conduct an extensive empirical study across various algorithmic considerations and describe practical strategies for scaling up expectation propagation to the modern federated setting. We apply FedEP on standard federated learning benchmarks and find that it outperforms strong baselines in terms of both convergence speed and accuracy. 1 Published as a conference paper at ICLR 2023 imate inference techniques. In this paper we turn to variational inference, in effect transforming the federated optimization problem into a distributed inference problem. Concretely, we view the solution of federated learning as the mode of a variational (posterior) distribution q ∈ Q with some divergence function D(· ·) (e.g., KL-divergence), θ = arg max θ q(θ), where q(θ) = arg min q∈Q D (p (θ | D) q (θ)) .Under this approach, clients use local computation to perform posterior inference (instead of parameter/gradient estimation) in parallel. In exchange, possibly fewer lockstep synchronization and communication steps are required between clients and servers.One way to operationalize Eq. 1 is through federated posterior averaging (FedPA, Al-Shedivat et al., 2021), where each client independently runs an approximate inference procedure and then sends the local posterior parameters to the server to be multiplicatively aggregated. However, there is no guarantee that independent approximations to local posteriors will lead to a good global approximate posterior. Motivated by the rich line of work on variational inference on streaming/partitioned data(Broderick et al., 2013;Vehtari et al., 2020), this work instead considers an expectation propagation (EP, Minka, 2001) approach to FL. In EP, each partition of the data maintains its own local contribution to the global posterior that is iteratively refined through probabilistic message-passing. When applied to FL, this results in an intuitive training scheme where at each round, each client (1) receives the current approximation to the global posterior from the centralized server, (2) carries out local inference to update its local approximation, and (3) sends the refined approximation to the server to be aggregated. Conceptually, this federated learning with expectation propagation (FedEP) approach extends FedPA by taking into account the current global approximation in step(2). | Published as a conference paper at ICLR 2023 FEDERATED LEARNING AS VARIATIONAL INFERENCE: A SCALABLE EXPECTATION PROPAGATION APPROACH |
d58004595 | This paper proposes a representational model for grid cells. In this model, the 2D self-position of the agent is represented by a high-dimensional vector, and the 2D self-motion or displacement of the agent is represented by a matrix that transforms the vector. Each component of the vector is a unit or a cell. The model consists of the following three sub-models. (1) Vector-matrix multiplication. The movement from the current position to the next position is modeled by matrix-vector multiplication, i.e., the vector of the next position is obtained by multiplying the matrix of the motion to the vector of the current position. (2) Magnified local isometry. The angle between two nearby vectors equals the Euclidean distance between the two corresponding positions multiplied by a magnifying factor. (3) Global adjacency kernel. The inner product between two vectors measures the adjacency between the two corresponding positions, which is defined by a kernel function of the Euclidean distance between the two positions. Our representational model has explicit algebra and geometry. It can learn hexagon patterns of grid cells, and it is capable of error correction, path integral and path planning. * Equal contributions. arXiv:1810.05597v3 [stat.ML] | LEARNING GRID CELLS AS VECTOR REPRESENTA- TION OF SELF-POSITION COUPLED WITH MATRIX REPRESENTATION OF SELF-MOTION |
d258298063 | MOBA games, e.g., Dota2 and Honor of Kings, have been actively used as the testbed for the recent AI research on games, and various AI systems have been developed at the human level so far. However, these AI systems mainly focus on how to compete with humans, less on exploring how to collaborate with humans. To this end, this paper makes the first attempt to investigate human-agent collaboration in MOBA games. In this paper, we propose to enable humans and agents to collaborate through explicit communication by designing an efficient and interpretable Meta-Command Communication-based framework, dubbed MCC, for accomplishing effective human-agent collaboration in MOBA games. The MCC framework consists of two pivotal modules: 1) an interpretable communication protocol, i.e., the Meta-Command, to bridge the communication gap between humans and agents; 2) a meta-command value estimator, i.e., the Meta-Command Selector, to select a valuable meta-command for each agent to achieve effective human-agent collaboration. Experimental results in Honor of Kings demonstrate that MCC agents can collaborate reasonably well with human teammates and even generalize to collaborate with different levels and numbers of human teammates. Videos are available at https://sites.google.com/view/mcc-demo. | Published as a conference paper at ICLR 2023 TOWARDS EFFECTIVE AND INTERPRETABLE HUMAN-AGENT COLLABORATION IN MOBA GAMES: A COMMUNICATION PERSPECTIVE |
d204824219 | Learning disentangled representations that correspond to factors of variation in real-world data is critical to interpretable and human-controllable machine learning. Recently, concerns about the viability of learning disentangled representations in a purely unsupervised manner has spurred a shift toward the incorporation of weak supervision. However, there is currently no formalism that identifies when and how weak supervision will guarantee disentanglement. To address this issue, we provide a theoretical framework to assist in analyzing the disentanglement guarantees (or lack thereof) conferred by weak supervision when coupled with learning algorithms based on distribution matching. We empirically verify the guarantees and limitations of several weak supervision methods (restricted labeling, match-pairing, and rank-pairing), demonstrating the predictive power and usefulness of our theoretical framework.1. We formalize weakly-supervised learning as distribution matching in an extended space. * Work done during an internship at Google Brain. arXiv:1910.09772v2 [cs.LG] 10 Apr 2020Published as a conference paper at ICLR 2020 2. We propose a set of definitions for disentanglement that can handle correlated factors and are inspired by many existing definitions in the literature(Higgins et al., 2018;Suter et al., 2018; Ridgeway & Mozer, 2018).3. Using these definitions, we provide a conceptually useful and theoretically rigorous calculus of disentanglement.4. We apply our theoretical framework of disentanglement to analyze three notable classes of weak supervision methods (restricted labeling, match pairing, and rank pairing). We show that although certain weak supervision methods (e.g., style-labeling in style-content disentanglement) do not guarantee disentanglement, our calculus can determine whether disentanglement is guaranteed when multiple sources of weak supervision are combined.5. Finally, we perform extensive experiments to systematically and empirically verify our predicted guarantees. 1FROM UNSUPERVISED TO WEAKLY SUPERVISED DISTRIBUTION MATCHINGOur goal in disentangled representation learning is to identify a latent-variable generative model whose latent variables correspond to ground truth factors of variation in the data. To identify the role that weak supervision plays in providing guarantees on disentanglement, we first formalize the model families we are considering, the forms of weak supervision, and finally the metrics we will use to evaluate and prove components of disentanglement.We consider data-generating processes where S ∈ R n are the factors of variation, with distribution p * (s), and X ∈ R m is the observed data point which is a deterministic function of S, i.e., X = g * (S). Many existing algorithms in unsupervised learning of disentangled representations aim to learn a latent-variable model with prior p(z) and generator g, where g(Z) d = g * (S). However, simply matching the marginal distribution over data is not enough: the learned latent variables Z and the true generating factors S could still be entangled with each other(Locatello et al., 2019).To address the failures of unsupervised learning of disentangled representations, we leverage weak supervision, where information about the data-generating process is conveyed through additional observations. By performing distribution matching on an augmented space (instead of just on the observation X), we can provide guarantees on learned representations. | Published as a conference paper at ICLR 2020 WEAKLY SUPERVISED DISENTANGLEMENT WITH GUARANTEES |
d53325983 | Detecting the emergence of abrupt property changes in time series is a challenging problem. Kernel two-sample test has been studied for this task which makes fewer assumptions on the distributions than traditional parametric approaches. However, selecting kernels is non-trivial in practice. Although kernel selection for two-sample test has been studied, the insufficient samples in change point detection problem hinders the success of those developed kernel selection algorithms. In this paper, we propose KL-CPD, a novel kernel learning framework for time series CPD that optimizes a lower bound of test power via an auxiliary generative model. With deep kernel parameterization, KL-CPD endows kernel twosample test with the data-driven kernel to detect different types of change-points in real-world applications. The proposed approach significantly outperformed other state-of-the-art methods in our comparative evaluation of benchmark datasets and simulation studies. | KERNEL CHANGE-POINT DETECTION WITH AUXIL- IARY DEEP GENERATIVE MODELS |
d259298789 | The dominant text generation models compose the output by sequentially selecting words from a fixed vocabulary.In this paper, we formulate text generation as progressively copying text segments (e.g., words or phrases) from an existing text collection.We compute the contextualized representations of meaningful text segments and index them using efficient vector search toolkits.The task of text generation is then decomposed into a series of copy-and-paste operations: at each time step, we seek suitable text spans from the text collection rather than selecting from a standalone vocabulary.Experiments on the standard language modeling benchmark (WikiText-103) show that our approach achieves better generation quality according to both automatic and human evaluations.Besides, its inference efficiency is comparable to token-level autoregressive models thanks to the reduction of decoding steps.We also show that our approach allows for effective domain adaptation by simply switching to domain-specific text collection without extra training.Finally, we observe that our approach attains additional performance gains by simply scaling up to larger text collections, again without further training. 1 | |
d232075995 | Compared to traditional visual question answering, video-grounded dialogues require additional reasoning over dialogue context to answer questions in a multiturn setting. Previous approaches to video-grounded dialogues mostly use dialogue context as a simple text input without modelling the inherent information flows at the turn level. In this paper, we propose a novel framework of Reasoning Paths in Dialogue Context (PDC). PDC model discovers information flows among dialogue turns through a semantic graph constructed based on lexical components in each question and answer. PDC model then learns to predict reasoning paths over this semantic graph. Our path prediction model predicts a path from the current turn through past dialogue turns that contain additional visual cues to answer the current question. Our reasoning model sequentially processes both visual and textual information through this reasoning path and the propagated features are used to generate the answer. Our experimental results demonstrate the effectiveness of our method and provide additional insights on how models use semantic dependencies in a dialogue context to retrieve visual cues. | Published as a conference paper at ICLR 2021 LEARNING REASONING PATHS OVER SEMANTIC GRAPHS FOR VIDEO-GROUNDED DIALOGUES |
d252968153 | We consider a setting that a model needs to adapt to a new domain under distribution shifts, given that only unlabeled test samples from the new domain are accessible at test time. A common idea in most of the related works is constructing pseudolabels for the unlabeled test samples and applying gradient descent (GD) to a loss function with the pseudo-labels. Recently, Goyal et al. (2022) propose conjugate labels, which is a new kind of pseudo-labels for self-training at test time. They empirically show that the conjugate label outperforms other ways of pseudolabeling on many domain adaptation benchmarks. However, provably showing that GD with conjugate labels learns a good classifier for test-time adaptation remains open. In this work, we aim at theoretically understanding GD with hard and conjugate labels for a binary classification problem. We show that for square loss, GD with conjugate labels converges to an -optimal predictor under a Gaussian model for any arbitrarily small , while GD with hard pseudo-labels fails in this task. We also analyze them under different loss functions for the update. Our results shed lights on understanding when and why GD with hard labels or conjugate labels works in test-time adaptation. | Published as a conference paper at ICLR 2023 TOWARDS UNDERSTANDING GD WITH HARD AND CONJUGATE PSEUDO-LABELS FOR TEST-TIME ADAP- TATION |
d17140888 | The problem of detecting and recognizing text in natural scenes has proved to be more challenging than its counterpart in documents, with most of the previous work focusing on a single part of the problem. In this work, we propose new solutions to the character and word recognition problems and then show how to combine these solutions in an end-to-end text-recognition system. We do so by leveraging the recently introduced Maxout networks along with hybrid HMM models that have proven useful for voice recognition. Using these elements, we build a tunable and highly accurate recognition system that beats state-of-theart results on all the sub-problems for both the ICDAR 2003 and SVT benchmark datasets. 1 | End-to-End Text Recognition with Hybrid HMM Maxout Models Ouais Alsharif |
d2780493 | Layer-sequential unit-variance (LSUV) initialization -a simple method for weight initialization for deep net learning -is proposed. The method consists of the two steps. First, pre-initialize weights of each convolution or inner-product layer with orthonormal matrices. Second, proceed from the first to the final layer, normalizing the variance of the output of each layer to be equal to one. Experiment with different activation functions (maxout, ReLU-family, tanh) show that the proposed initialization leads to learning of very deep nets that (i) produces networks with test accuracy better or equal to standard methods and (ii) is at least as fast as the complex schemes proposed specifically for very deep nets such as FitNets(Romero et al. (2015)) and Highway (Srivastava et al.(2015)). Performance is evaluated on GoogLeNet, CaffeNet, FitNets and Residual nets and the state-of-the-art, or very close to it, is achieved on the MNIST, CIFAR-10/100 and ImageNet datasets. 1 The code allowing to reproduce the experiments is available at | ALL YOU NEED IS A GOOD INIT |
d2134321 | Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, we introduce "deep compression", a three stage pipeline: pruning, trained quantization and Huffman coding, that work together to reduce the storage requirement of neural networks by 35× to 49× without affecting their accuracy. Our method first prunes the network by learning only the important connections. Next, we quantize the weights to enforce weight sharing, finally, we apply Huffman coding. After the first two steps we retrain the network to fine tune the remaining connections and the quantized centroids. Pruning, reduces the number of connections by 9× to 13×; Quantization then reduces the number of bits that represent each connection from 32 to 5. On the ImageNet dataset, our method reduced the storage required by AlexNet by 35×, from 240MB to 6.9MB, without loss of accuracy. Our method reduced the size of VGG-16 by 49× from 552MB to 11.3MB, again with no loss of accuracy. This allows fitting the model into on-chip SRAM cache rather than off-chip DRAM memory. Our compression method also facilitates the use of complex neural networks in mobile applications where application size and download bandwidth are constrained. Benchmarked on CPU, GPU and mobile GPU, compressed network has 3× to 4× layerwise speedup and 3× to 7× better energy efficiency. | Published as a conference paper at ICLR 2016 DEEP COMPRESSION: COMPRESSING DEEP NEURAL NETWORKS WITH PRUNING, TRAINED QUANTIZATION AND HUFFMAN CODING |
d214536507 | We introduce LiPopt, a polynomial optimization framework for computing increasingly tighter upper bounds on the Lipschitz constant of neural networks. The underlying optimization problems boil down to either linear (LP) or semidefinite (SDP) programming. We show how to use the sparse connectivity of a network, to significantly reduce the complexity of computation. This is specially useful for convolutional as well as pruned neural networks. We conduct experiments on networks with random weights as well as networks trained on MNIST, showing that in the particular case of the ∞ -Lipschitz constant, our approach yields superior estimates, compared to baselines available in the literature.arXiv:2004.08688v1 [cs.LG] 18 Apr 2020Published as a conference paper at ICLR 2020 to compute, these type of certificates are in practice surprisingly tight. Our work belongs in this vein of research, and aims to overcome some limitations in the current state-of-the-art.Our Contributions.We present LiPopt, a general approach for upper bounding the Lipschitz constant of a neural network based on a relaxation to a polynomial optimization problem (POP)(Lasserre, 2015). This approach requires only that the unit ball be described with polynomial inequalities, which covers the common 2 -and ∞ -norms. Based on a theorem due toWeisser et al. (2018), we exploit the sparse connectivity of neural network architectures to derive a sequence of linear programs (LPs) of considerably smaller size than their vanilla counterparts. We provide an asymptotic analysis of the size of such programs, in terms of the number of neurons, depth and sparsity of the network. Focusing on the ∞ -norm, we experiment on networks with random weights and networks trained on MNIST (Lecun et al., 1998). We evaluate different configurations of depth, width and sparsity and we show that the proposed sequence of LPs can provide tighter upper bounds on L(f d ) compared to other baselines available in the literature. | LIPSCHITZ CONSTANT ESTIMATION OF NEURAL NET- WORKS VIA SPARSE POLYNOMIAL OPTIMIZATION |
d3509328 | In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions. It directly models the probability distribution of generating a word given previous words and an image. Image captions are generated according to this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on four benchmark datasets (IAPR TC-12, Flickr 8K, Flickr 30K and MS COCO). Our model outperforms the state-of-the-art methods. In addition, the m-RNN model can be applied to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval. 1 | DEEP CAPTIONING WITH MULTIMODAL RECURRENT NEURAL NETWORKS (M-RNN) |
d257377961 | Controlling agents remotely with deep reinforcement learning (DRL) in the real world is yet to come. One crucial stepping stone is to devise RL algorithms that are robust in the face of dropped information from corrupted communication or malfunctioning sensors. Typical RL methods usually require considerable online interaction data that are costly and unsafe to collect in the real world. Furthermore, when applying to the frame dropping scenarios, they perform unsatisfactorily even with moderate drop rates. To address these issues, we propose Decision Transformer under Random Frame Dropping (DeFog), an offline RL algorithm that enables agents to act robustly in frame dropping scenarios without online interaction. DeFog first randomly masks out data in the offline datasets and explicitly adds the time span of frame dropping as inputs. After that, a finetuning stage on the same offline dataset with a higher mask rate would further boost the performance. Empirical results show that DeFog outperforms strong baselines under severe frame drop rates like 90%, while maintaining similar returns under non-frame-dropping conditions in the regular MuJoCo control benchmarks and the Atari environments. Our approach offers a robust and deployable solution for controlling agents in real-world environments with limited or unreliable data. | Published as a conference paper at ICLR 2023 DECISION TRANSFORMER UNDER RANDOM FRAME DROPPING |
d257020074 | Structural information of phylogenetic tree topologies plays an important role in phylogenetic inference. However, finding appropriate topological structures for specific phylogenetic inference tasks often requires significant design effort and domain expertise. In this paper, we propose a novel structural representation method for phylogenetic inference based on learnable topological features. By combining the raw node features that minimize the Dirichlet energy with modern graph representation learning techniques, our learnable topological features can provide efficient structural information of phylogenetic trees that automatically adapts to different downstream tasks without requiring domain expertise. We demonstrate the effectiveness and efficiency of our method on a simulated data tree probability estimation task and a benchmark of challenging real data variational Bayesian phylogenetic inference problems. | Published as a conference paper at ICLR 2023 LEARNABLE TOPOLOGICAL FEATURES FOR PHYLOGE- NETIC INFERENCE VIA GRAPH NEURAL NETWORKS |
d11217889 | While neural networks have achieved high accuracy on standard image classification benchmarks, their accuracy drops to nearly zero in the presence of small adversarial perturbations to test inputs. Defenses based on regularization and adversarial training have been proposed, but often followed by new, stronger attacks that defeat these defenses. Can we somehow end this arms race? In this work, we study this problem for neural networks with one hidden layer. We first propose a method based on a semidefinite relaxation that outputs a certificate that for a given network and test input, no attack can force the error to exceed a certain value. Second, as this certificate is differentiable, we jointly optimize it with the network parameters, providing an adaptive regularizer that encourages robustness against all attacks. On MNIST, our approach produces a network and a certificate that no attack that perturbs each pixel by at most = 0.1 can cause more than 35% test error. | Under review as a conference paper at ICLR 2018 CERTIFIED DEFENSES AGAINST ADVERSARIAL EX- AMPLES |
d260440449 | Transformers do not scale very well to long sequence lengths largely because of quadratic self-attention complexity. In the recent months, a wide spectrum of efficient, fast Transformers have been proposed to tackle this problem, more often than not claiming superior or comparable model quality to vanilla Transformer models. To this date, there is no well-established consensus on how to evaluate this class of models. Moreover, inconsistent benchmarking on a wide spectrum of tasks and datasets makes it difficult to assess relative model quality amongst many models. This paper proposes a systematic and unified benchmark, Long-Range Arena, specifically focused on evaluating model quality under long-context scenarios. Our benchmark is a suite of tasks consisting of sequences ranging from 1K to 16K tokens, encompassing a wide range of data types and modalities such as text, natural, synthetic images, and mathematical expressions requiring similarity, structural, and visual-spatial reasoning. We systematically evaluate ten well-established long-range Transformer models (Reformers, Linformers, Linear Transformers, Sinkhorn Transformers, Performers, Synthesizers, Sparse Transformers, and Longformers) on our newly proposed benchmark suite. Long-Range Arena paves the way towards better understanding this class of efficient Transformer models, facilitates more research in this direction, and presents new challenging tasks to tackle. Our benchmark code will be released at https://github.com/google-research/long-range-arena. * First two authors contributed equally. . Learning long-range spatial dependencies with horizontal gated recurrent units. In Advances in neural information processing systems, pp. 152-164, 2018. | LONG RANGE ARENA: A BENCHMARK FOR EFFICIENT TRANSFORMERS |
d232257874 | Recent works in Generative Adversarial Networks (GANs) are actively revisiting various data augmentation techniques as an effective way to prevent discriminator overfitting. It is still unclear, however, that which augmentations could actually improve GANs, and in particular, how to apply a wider range of augmentations in training. In this paper, we propose a novel way to address these questions by incorporating a recent contrastive representation learning scheme into the GAN discriminator, coined ContraD. This "fusion" enables the discriminators to work with much stronger augmentations without increasing their training instability, thereby preventing the discriminator overfitting issue in GANs more effectively. Even better, we observe that the contrastive learning itself also benefits from our GAN training, i.e., by maintaining discriminative features between real and fake samples, suggesting a strong coherence between the two worlds: good contrastive representations are also good for GAN discriminators, and vice versa. Our experimental results show that GANs with ContraD consistently improve FID and IS compared to other recent techniques incorporating data augmentations, still maintaining highly discriminative features in the discriminator in terms of the linear evaluation. Finally, as a byproduct, we also show that our GANs trained in an unsupervised manner (without labels) can induce many conditional generative models via a simple latent sampling, leveraging the learned features of ContraD. Code is available at https | Published as a conference paper at ICLR 2021 TRAINING GANS WITH STRONGER AUGMENTATIONS VIA CONTRASTIVE DISCRIMINATOR |
d225075683 | Safe exploration presents a major challenge in reinforcement learning (RL): when active data collection requires deploying partially trained policies, we must ensure that these policies avoid catastrophically unsafe regions, while still enabling trial and error learning. In this paper, we target the problem of safe exploration in RL by learning a conservative safety estimate of environment states through a critic, and provably upper bound the likelihood of catastrophic failures at every training iteration. We theoretically characterize the tradeoff between safety and policy improvement, show that the safety constraints are likely to be satisfied with high probability during training, derive provable convergence guarantees for our approach, which is no worse asymptotically than standard RL, and demonstrate the efficacy of the proposed approach on a suite of challenging navigation, manipulation, and locomotion tasks. Empirically, we show that the proposed approach can achieve competitive task performance while incurring significantly lower catastrophic failure rates during training than prior methods. Videos are at this url https://sites.google.com | Preprint. Under review CONSERVATIVE SAFETY CRITICS FOR EXPLORATION |
d228084090 | In many scenarios, named entity recognition (NER) models severely suffer from unlabeled entity problem, where the entities of a sentence may not be fully annotated. Through empirical studies performed on synthetic datasets, we find two causes of the performance degradation. One is the reduction of annotated entities and the other is treating unlabeled entities as negative instances. The first cause has less impact than the second one and can be mitigated by adopting pretraining language models. The second cause seriously misguides a model in training and greatly affects its performances. Based on the above observations, we propose a general approach that is capable of eliminating the misguidance brought by unlabeled entities. The core idea is using negative sampling to keep the probability of training with unlabeled entities at a very low level. Experiments on synthetic datasets and real-world datasets show that our model is robust to unlabeled entity problem and surpasses prior baselines. On well-annotated datasets, our model is competitive with state-of-the-art method 1 . | EMPIRICAL ANALYSIS OF UNLABELED ENTITY PROB- LEM IN NAMED ENTITY RECOGNITION |
d3334133 | It is by now well-known that small adversarial perturbations can induce classification errors in deep neural networks (DNNs). In this paper, we make the case that sparse representations of the input data are a crucial tool for combating such attacks. For linear classifiers, we show that a sparsifying front end is provably effective against ∞ -bounded attacks, reducing output distortion due to the attack by a factor of roughly K/N where N is the data dimension and K is the sparsity level. We then extend this concept to DNNs, showing that a "locally linear" model can be used to develop a theoretical foundation for crafting attacks and defenses. Experimental results for the MNIST dataset show the efficacy of the proposed sparsifying front end. * Joint first authors. | Workshop track -ICLR 2018 COMBATING ADVERSARIAL ATTACKS USING SPARSE REPRESENTATIONS |
d232233782 | Label noise is frequently observed in real-world large-scale datasets. The noise is introduced due to a variety of reasons; it is heterogeneous and feature-dependent. Most existing approaches to handling noisy labels fall into two categories: they either assume an ideal feature-independent noise, or remain heuristic without theoretical guarantees. In this paper, we propose to target a new family of featuredependent label noise, which is much more general than commonly used i.i.d. label noise and encompasses a broad spectrum of noise patterns. Focusing on this general noise family, we propose a progressive label correction algorithm that iteratively corrects labels and refines the model. We provide theoretical guarantees showing that for a wide variety of (unknown) noise patterns, a classifier trained with this strategy converges to be consistent with the Bayes classifier. In experiments, our method outperforms SOTA baselines and is robust to various noise types and levels. * Equal contributions. | Published as a conference paper at ICLR 2021 LEARNING WITH FEATURE-DEPENDENT LABEL NOISE: A PROGRESSIVE APPROACH |
d231861410 | Machine learning benefits from large training datasets, which may not always be possible to collect by any single entity, especially when using privacy-sensitive data. In many contexts, such as healthcare and finance, separate parties may wish to collaborate and learn from each other's data but are prevented from doing so due to privacy regulations. Some regulations prevent explicit sharing of data between parties by joining datasets in a central location (confidentiality). Others also limit implicit sharing of data, e.g., through model predictions (privacy). There is currently no method that enables machine learning in such a setting, where both confidentiality and privacy need to be preserved, to prevent both explicit and implicit sharing of data. Federated learning only provides confidentiality, not privacy, since gradients shared still contain private information. Differentially private learning assumes unreasonably large datasets. Furthermore, both of these learning paradigms produce a central model whose architecture was previously agreed upon by all parties rather than enabling collaborative learning where each party learns and improves their own local model. We introduce Confidential and Private Collaborative (CaPC) learning, the first method provably achieving both confidentiality and privacy in a collaborative setting. We leverage secure multiparty computation (MPC), homomorphic encryption (HE), and other techniques in combination with privately aggregated teacher models. We demonstrate how CaPC allows participants to collaborate without having to explicitly join their training sets or train a central model. Each party is able to improve the accuracy and fairness of their model, even in settings where each party has a model that performs well on their own dataset or when datasets are not IID and model architectures are heterogeneous across parties. . Smote: synthetic minority over-sampling technique. | Published as a conference paper at ICLR 2021 CAPC LEARNING: CONFIDENTIAL AND PRIVATE COLLABORATIVE LEARNING |
d255595956 | Our brain can almost effortlessly decompose visual data streams into background and salient objects. Moreover, it can anticipate object motion and interactions, which are crucial abilities for conceptual planning and reasoning. Recent object reasoning datasets, such as CATER, have revealed fundamental shortcomings of current vision-based AI systems, particularly when targeting explicit object representations, object permanence, and object reasoning. Here we introduce a self-supervised LOCation and Identity tracking system (Loci), which excels on the CATER tracking challenge. Inspired by the dorsal and ventral pathways in the brain, Loci tackles the binding problem by processing separate, slot-wise encodings of 'what' and 'where'. Loci's predictive coding-like processing encourages active error minimization, such that individual slots tend to encode individual objects. Interactions between objects and object dynamics are processed in the disentangled latent space. Truncated backpropagation through time combined with forward eligibility accumulation significantly speeds up learning and improves memory efficiency. Besides exhibiting superior performance in current benchmarks, Loci effectively extracts objects from video streams and separates them into location and Gestalt components. We believe that this separation offers a representation that will facilitate effective planning and reasoning on conceptual levels. 1 | LEARNING WHAT AND WHERE: DISENTANGLING LOCATION AND IDENTITY TRACK- ING WITHOUT SUPERVISION |
d252211791 | Adversarial patch attacks are an emerging security threat for real world deep learning applications. We present DEMASKED SMOOTHING, the first approach (up to our knowledge) to certify the robustness of semantic segmentation models against this threat model. Previous work on certifiably defending against patch attacks has mostly focused on image classification task and often required changes in the model architecture and additional training which is undesirable and computationally expensive. In DEMASKED SMOOTHING, any segmentation model can be applied without particular training, fine-tuning, or restriction of the architecture. Using different masking strategies, DEMASKED SMOOTHING can be applied both for certified detection and certified recovery. In extensive experiments we show that DEMASKED SMOOTHING can on average certify 63% of the pixel predictions for a 1% patch in the detection task and 46% against a 0.5% patch for the recovery task on the ADE20K dataset. | Published as a conference paper at ICLR 2023 CERTIFIED DEFENCES AGAINST ADVERSARIAL PATCH ATTACKS ON SEMANTIC SEGMENTATION |
d209483461 | Binary Neural Networks (BNNs) have been garnering interest thanks to their compute cost reduction and memory savings. However, BNNs suffer from performance degradation mainly due to the gradient mismatch caused by binarizing activations. Previous works tried to address the gradient mismatch problem by reducing the discrepancy between activation functions used at forward pass and its differentiable approximation used at backward pass, which is an indirect measure. In this work, we use the gradient of smoothed loss function to better estimate the gradient mismatch in quantized neural network. Analysis using the gradient mismatch estimator indicates that using higher precision for activation is more effective than modifying the differentiable approximation of activation function. Based on the observation, we propose a new training scheme for binary activation networks called BinaryDuo in which two binary activations are coupled into a ternary activation during training. Experimental results show that BinaryDuo outperforms state-of-the-art BNNs on various benchmarks with the same amount of parameters and computing cost. * Hyungjun Kim and Kyungsu Kim equally contributed to this work. | BINARYDUO: REDUCING GRADIENT MISMATCH IN BI- NARY ACTIVATION NETWORK BY COUPLING BINARY ACTIVATIONS |
d2922805 | We propose BlackOut, an approximation algorithm to efficiently train massive recurrent neural network language models (RNNLMs) with million word vocabularies. BlackOut is motivated by using a discriminative loss, and we describe a weighted sampling strategy which significantly reduces computation while improving stability, sample efficiency, and rate of convergence. One way to understand BlackOut is to view it as an extension of the DropOut strategy to the output layer, wherein we use a discriminative training loss and a weighted sampling scheme. We also establish close connections between BlackOut, importance sampling, and noise contrastive estimation (NCE). Our experiments, on the recently released one billion word language modeling benchmark, demonstrate scalability and accuracy of BlackOut; we outperform the state-of-the art, and achieve the lowest perplexity scores on this dataset. Moreover, unlike other established methods which typically require GPUs or CPU clusters, we show that a carefully implemented version of BlackOut requires only 1-10 days on a single machine to train a RNNLM with a million word vocabulary and billions of parameters on one billion words. Although we describe BlackOut in the context of RNNLM training, it can be used to any networks with large softmax output layers. | BLACKOUT: SPEEDING UP RECURRENT NEURAL NET- WORK LANGUAGE MODELS WITH VERY LARGE VO- CABULARIES |
d16209268 | Training neural networks involves solving large-scale non-convex optimization problems. This task has long been believed to be extremely difficult, with fear of local minima and other obstacles motivating a variety of schemes to improve optimization, such as unsupervised pretraining. However, modern neural networks are able to achieve negligible training error on complex tasks, using only direct training with stochastic gradient descent. We introduce a simple analysis technique to look for evidence that such networks are overcoming local optima. We find that, in fact, on a straight path from initialization to solution, a variety of state of the art neural networks never encounter any significant obstacles. | Published as a conference paper at ICLR 2015 QUALITATIVELY CHARACTERIZING NEURAL NETWORK OPTIMIZATION PROBLEMS |
d231985837 | To explore the vulnerability of deep neural networks (DNNs), many attack paradigms have been well studied, such as the poisoning-based backdoor attack in the training stage and the adversarial attack in the inference stage. In this paper, we study a novel attack paradigm, which modifies model parameters in the deployment stage for malicious purposes. Specifically, our goal is to misclassify a specific sample into a target class without any sample modification, while not significantly reduce the prediction accuracy of other samples to ensure the stealthiness. To this end, we formulate this problem as a binary integer programming (BIP), since the parameters are stored as binary bits (i.e., 0 and 1) in the memory. By utilizing the latest technique in integer programming, we equivalently reformulate this BIP problem as a continuous optimization problem, which can be effectively and efficiently solved using the alternating direction method of multipliers (ADMM) method. Consequently, the flipped critical bits can be easily determined through optimization, rather than using a heuristic strategy. Extensive experiments demonstrate the superiority of our method in attacking DNNs. The . Boosting decision-based blackbox adversarial attacks with random sign flip. In . On the douglas-rachford splitting method and the proximal point algorithm for maximal monotone operators. Mathematical Programming, 55(1-3):293-318, 1992. attack on deep product quantization network for image retrieval. In AAAI, 2020. Daniel Gabay and Bertrand Mercier. A dual algorithm for the solution of nonlinear variational problems via finite element approximation. Computers & mathematics with applications, 2(1): 17-40, 1976. Roland Glowinski and A Marroco. Sur l'approximation, paréléments finis d'ordre un, et la résolution, par pénalisation-dualité d'une classe de problèmes de dirichlet non linéaires. ESAIM: Mathematical Modelling and Numerical Analysis-Modélisation Mathématique et Analyse Numérique, 9(R2):41-76, 1975. E Gi Gol'shtein and NV Tret'yakov. Modified lagrangians in convex programming and their generalizations. In | Published as a conference paper at ICLR 2021 TARGETED ATTACK AGAINST DEEP NEURAL NET- WORKS VIA FLIPPING LIMITED WEIGHT BITS |
d212644628 | Accurate models of the world are built upon notions of its underlying symmetries. In physics, these symmetries correspond to conservation laws, such as for energy and momentum. Yet even though neural network models see increasing use in the physical sciences, they struggle to learn these symmetries. In this paper, we propose Lagrangian Neural Networks (LNNs), which can parameterize arbitrary Lagrangians using neural networks. In contrast to models that learn Hamiltonians, LNNs do not require canonical coordinates, and thus perform well in situations where canonical momenta are unknown or difficult to compute. Unlike previous approaches, our method does not restrict the functional form of learned energies and will produce energy-conserving models for a variety of tasks. We test our approach on a double pendulum and a relativistic particle, demonstrating energy conservation where a baseline approach incurs dissipation and modeling relativity without canonical coordinates where a Hamiltonian approach fails. Finally, we show how this model can be applied to graphs and continuous systems using a Lagrangian Graph Network, and demonstrate it on the 1D wave equation. * Also affiliated with Princeton University | LAGRANGIAN NEURAL NETWORKS |
d222272305 | Despite recent successes of reinforcement learning (RL), it remains a challenge for agents to transfer learned skills to related environments. To facilitate research addressing this problem, we propose CausalWorld, a benchmark for causal structure and transfer learning in a robotic manipulation environment. The environment is a simulation of an open-source robotic platform, hence offering the possibility of sim-to-real transfer. Tasks consist of constructing 3D shapes from a given set of blocks -inspired by how children learn to build complex structures. The key strength of CausalWorld is that it provides a combinatorial family of such tasks with common causal structure and underlying factors (including, e.g., robot and object masses, colors, sizes). The user (or the agent) may intervene on all causal variables, which allows for fine-grained control over how similar different tasks (or task distributions) are. One can thus easily define training and evaluation distributions of a desired difficulty level, targeting a specific form of generalization (e.g., only changes in appearance or object mass). Further, this common parametrization facilitates defining curricula by interpolating between an initial and a target task. While users may define their own task distributions, we present eight meaningful distributions as concrete benchmarks, ranging from simple to very challenging, all of which require long-horizon planning as well as precise low-level motor control. Finally, we provide baseline results for a subset of these tasks on distinct training curricula and corresponding evaluation protocols, verifying the feasibility of the tasks in this benchmark. 4 0 * Equal Contribution | CAUSALWORLD: A ROBOTIC MANIPULATION BENCHMARK FOR CAUSAL STRUCTURE AND TRANS- FER LEARNING |
d249209650 | Many real-world settings involve costs for performing actions; transaction costs in financial systems and fuel costs being common examples. In these settings, performing actions at each time step quickly accumulates costs leading to vastly suboptimal outcomes. Additionally, repeatedly acting produces wear and tear and ultimately, damage. Determining when to act is crucial for achieving successful outcomes and yet, the challenge of efficiently learning to behave optimally when actions incur minimally bounded costs remains unresolved. In this paper, we introduce a reinforcement learning (RL) framework named Learnable Impulse Control Reinforcement Algorithm (LICRA), for learning to optimally select both when to act and which actions to take when actions incur costs. At the core of LICRA is a nested structure that combines RL and a form of policy known as impulse control which learns to maximise objectives when actions incur costs. We prove that LICRA, which seamlessly adopts any RL method, converges to policies that optimally select when to perform actions and their optimal magnitudes. We then augment LICRA to handle problems in which the agent can perform at most k < ∞ actions and more generally, faces a budget constraint. We show LICRA learns the optimal value function and ensures budget constraints are satisfied almost surely. We demonstrate empirically LICRA's superior performance against benchmark RL methods in OpenAI gym's Lunar Lander and in Highway environments and a variant of the Merton portfolio problem within finance. . Portfolio selection with transaction costs. Mathematics of operations research, 15(4): 1990.Abderrahim Fathan and Erick Delage. Deep reinforcement learning for optimal stopping with application in financial engineering. arXiv preprint arXiv:2105.08877, 2021.F Grandt Jr. Damage tolerant design and nondestructive inspection-keys to aircraft airworthiness. , et al. Soft actor-critic algorithms and applications. arXiv preprint arXiv:1812.05905, 2018. | Published as a conference paper at ICLR 2023 TIMING IS EVERYTHING: LEARNING TO ACT SELEC- TIVELY WITH COSTLY ACTIONS AND CONSTRAINTS |
d52901322 | Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering. Many metric learning methods represent the input as a single point in the embedding space. Often the distance between points is used as a proxy for match confidence. However, this can fail to represent uncertainty which can arise when the input is ambiguous, e.g., due to occlusion or blurriness. This work addresses this issue and explicitly models the uncertainty by "hedging" the location of each input in the embedding space. We introduce the hedged instance embedding (HIB) in which embeddings are modeled as random variables and the model is trained under the variational information bottleneck principle(Alemi et al., 2016;Achille & Soatto, 2018). Empirical results on our new N-digit MNIST dataset show that our method leads to the desired behavior of "hedging its bets" across the embedding space upon encountering ambiguous inputs. This results in improved performance for image matching and classification tasks, more structure in the learned embedding space, and an ability to compute a per-exemplar uncertainty measure which is correlated with downstream performance. * Work performed during an internship with Google Research. 1 arXiv:1810.00319v4 [cs.LG] 21 Dec 2018 (a) Point embedding. (b) Stochastic embedding. | MODELING UNCERTAINTY WITH HEDGED INSTANCE EMBEDDING |
d252734863 | The application of pre-training large transformer models on massive amounts of unlabeled data and fine-tuning them on labeled datasets for diverse downstream tasks has demonstrated remarkable success in various vision and natural language processing tasks. However, the direct fine-tuning approach may result in suboptimal performance if there exists a significant discrepancy between the pre-training and fine-tuning domains. To address this issue, some previous studies have proposed further pre-training strategies to continue pre-training the model on the target unlabeled dataset before fine-tuning. However, these strategies are limited to language models and may result in overfitting when applied to Vision Transformers. To overcome this limitation, we present a novel approach of self-distillation as a regularization method for the further pre-training stage. Our method first further pre-trains the initial pre-trained model on the target unlabeled data, and then uses it as a teacher for self-distillation. Then we take the same initial pre-trained model as a student, and enforce its hidden representations to be close to those of the teacher while optimizing the student with a masked auto-encoding objective. Our experiments demonstrate the superiority of self-distillation over relevant baselines on various benchmark datasets for image and text classification tasks. Furthermore, we provide a theoretical analysis of our proposed method using a simplified model to shed light on how self-distillation for further pre-training can potentially enhance the performance of downstream tasks. | Published as a conference paper at ICLR 2023 SELF-DISTILLATION FOR FURTHER PRE-TRAINING OF TRANSFORMERS |
d1369182 | We develop a metalearning approach for learning hierarchically structured policies, improving sample efficiency on unseen tasks through the use of shared primitives-policies that are executed for large numbers of timesteps. Specifically, a set of primitives are shared within a distribution of tasks, and are switched between by task-specific policies. We provide a concrete metric for measuring the strength of such hierarchies, leading to an optimization problem for quickly reaching high reward on unseen tasks. We then present an algorithm to solve this problem end-to-end through the use of any off-the-shelf reinforcement learning method, by repeatedly sampling new tasks and resetting task-specific policies. We successfully discover 1 meaningful motor primitives for the directional movement of four-legged robots, solely by interacting with distributions of mazes. We also demonstrate the transferability of primitives to solve long-timescale sparse-reward obstacle courses, and we enable 3D humanoid robots to robustly walk and crawl with the same policy. | META LEARNING SHARED HIERARCHIES Work done as an intern at OpenAI |
d13669032 | We propose a novel regularizer to improve the training of Generative Adversarial Networks (GANs). The motivation is that when the discriminator D spreads out its model capacity in the right way, the learning signals given to the generator G are more informative and diverse. These in turn help G to explore better and discover the real data manifold while avoiding large unstable jumps due to the erroneous extrapolation made by D . Our regularizer guides the rectifier discriminator D to better allocate its model capacity, by encouraging the binary activation patterns on selected internal layers of D to have a high joint entropy. Experimental results on both synthetic data and real datasets demonstrate improvements in stability and convergence speed of the GAN training, as well as higher sample quality. The approach also leads to higher classification accuracies in semi-supervised learning. | Published as a conference paper at ICLR 2018 IMPROVING GAN TRAINING VIA BINARIZED REPRESENTATION ENTROPY (BRE) REGULARIZATION |
d257102642 | To protect user privacy and meet legal regulations, federated learning (FL) is attracting significant attention. Training neural machine translation (NMT) models with traditional FL algorithms (e.g., FedAvg) typically relies on multi-round model-based interactions. However, it is impractical and inefficient for translation tasks due to the vast communication overheads and heavy synchronization. In this paper, we propose a novel Federated Nearest Neighbor (FedNN) machine translation framework that, instead of multi-round model-based interactions, leverages one-round memorization-based interaction to share knowledge across different clients and build low-overhead privacy-preserving systems. The whole approach equips the public NMT model trained on large-scale accessible data with a k-nearestneighbor (kNN) classifier and integrates the external datastore constructed by private text data from all clients to form the final FL model. A two-phase datastore encryption strategy is introduced to achieve privacy-preserving during this process. Extensive experiments show that FedNN significantly reduces computational and communication costs compared with FedAvg, while maintaining promising translation performance in different FL settings.Published as a conference paper at ICLR 2023 and introduce some parameter pruning strategies during node communication. Despite this, multi-round model-based interactions are impractical and inefficient for NMT applications. Current models heavily rely on deep neural networks as the backbone and their parameters can reach tens of millions or even hundreds of millions, bringing vast computation and communication overhead. In real-world scenarios, different clients (i.e., users and enterprises) usually have limited computation and communication capabilities, making it difficult to meet frequent model training and node communication requirements in the standard FL workflow. Further, due to the capability differences between clients, heavy synchronization also hinders the efficacy of FL workflow. Fewer interactions may ease this problem but suffer from significant performance loss.Inspired by the recent remarkable performance of memorization-augmented techniques (e.g., the k-nearestneighbor, kNN) in natural language processing(Khandelwal et al., 2020;Zheng et al., 2021a;b) and computer vision (Papernot & Mcdaniel, 2018;Orhan, 2018), we take a new perspective to deal with above federated NMT training problem. In this paper, we propose a novel Federated Nearest Neighbor (FedNN) machine translation framework, which equips the public NMT model trained on large-scale accessible data with a kNN classifier and integrates the external datastore constructed by private data from all clients to form the final FL model. In this way, we replace the multi-round model-based interactions in the conventional FL paradigm with the one-round encrypted memorization-based interaction to share knowledge among different clients and drastically reduce computation and communication overhead. | Published as a conference paper at ICLR 2023 FEDERATED NEAREST NEIGHBOR MACHINE TRANSLATION |
d249395677 | Recent approaches in self-supervised learning of image representations can be categorized into different families of methods and, in particular, can be divided into contrastive and non-contrastive approaches. While differences between the two families have been thoroughly discussed to motivate new approaches, we focus more on the theoretical similarities between them. By designing contrastive and covariance based non-contrastive criteria that can be related algebraically and shown to be equivalent under limited assumptions, we show how close those families can be. We further study popular methods and introduce variations of them, allowing us to relate this theoretical result to current practices and show the influence (or lack thereof) of design choices on downstream performance. Motivated by our equivalence result, we investigate the low performance of SimCLR and show how it can match VICReg's with careful hyperparameter tuning, improving significantly over known baselines. We also challenge the popular assumption that non-contrastive methods need large output dimensions. Our theoretical and quantitative results suggest that the numerical gaps between contrastive and noncontrastive methods in certain regimes can be closed given better network design choices and hyperparameter tuning. The evidence shows that unifying different SOTA methods is an important direction to build a better understanding of selfsupervised learning. , in no particular order, for insightful discussions. We also thank Florian Bordes for the efficient implementations that were used for our experiments.REPRODUCIBILITY STATEMENTWhile our pretrainings are very costly, each taking around a day with 8 V100 GPUs, we provide complete hyperparameter values in table S6. They are compatible with official implementations of the losses, and for VICReg-ctr and VICReg-exp we also provide PyTorch pseudocode in supplementary section L. In order to reproduce our main figure, we also give the numerical performance in table S5. All of this should make our results reproducible, and, more importantly, should make it so that practitioners can benefit from the improved performance that we introduce.REFERENCESRandall Balestriero and Yann LeCun. Contrastive and non-contrastive self-supervised learning recover global and local spectral embedding methods. arXiv preprint arXiv:2205.11508, 2022.Adrien Bardes, Jean Ponce, and Yann LeCun. Vicreg: Variance-invariance-covariance regularization for self-supervised learning. arXiv preprint arXiv:2105.04906, 2021. | Published as a conference paper at ICLR 2023 ON THE DUALITY BETWEEN CONTRASTIVE AND NON- CONTRASTIVE SELF-SUPERVISED LEARNING |
d14538467 | The recently introduced dropout training criterion for neural networks has been the subject of much attention due to its simplicity and remarkable effectiveness as a regularizer, as well as its interpretation as a training procedure for an exponentially large ensemble of networks that share parameters. In this work we empirically investigate several questions related to the efficacy of dropout, specifically as it concerns networks employing the popular rectified linear activation function.We investigate the quality of the test time weight-scaling inference procedure by evaluating the geometric average exactly in small models, as well as compare the performance of the geometric mean to the arithmetic mean more commonly employed by ensemble techniques. We explore the effect of tied weights on the ensemble interpretation by training ensembles of masked networks without tied weights. Finally, we investigate an alternative criterion based on a biased estimator of the maximum likelihood ensemble gradient. | An empirical analysis of dropout in piecewise linear networks |
d257631760 | Fine-tuning large pre-trained language models on downstream tasks has become an important paradigm in NLP. However, common practice fine-tunes all of the parameters in a pre-trained model, which becomes prohibitive when a large number of downstream tasks are present. Therefore, many fine-tuning methods are proposed to learn incremental updates of pre-trained weights in a parameter efficient way, e.g., low-rank increments. These methods often evenly distribute the budget of incremental updates across all pre-trained weight matrices, and overlook the varying importance of different weight parameters. As a consequence, the finetuning performance is suboptimal. To bridge this gap, we propose AdaLoRA, which adaptively allocates the parameter budget among weight matrices according to their importance score. In particular, AdaLoRA parameterizes the incremental updates in the form of singular value decomposition. Such a novel approach allows us to effectively prune the singular values of unimportant updates, which is essentially to reduce their parameter budget but circumvent intensive exact SVD computations. We conduct extensive experiments with several pre-trained models on natural language processing, question answering, and natural language generation to validate the effectiveness of AdaLoRA. Results demonstrate that AdaLoRA manifests notable improvement over baselines, especially in the low budget settings. Our code is publicly available at https://github.com/ QingruZhang/AdaLoRA. | Published as a conference paper at ICLR 2023 ADAPTIVE BUDGET ALLOCATION FOR PARAMETER- EFFICIENT FINE-TUNING |
d257102667 | The transferability of adversarial perturbations between image models has been extensively studied. In this case, an attack is generated from a known surrogate e.g., the ImageNet trained model, and transferred to change the decision of an unknown (black-box) model trained on an image dataset. However, attacks generated from image models do not capture the dynamic nature of a moving object or a changing scene due to a lack of temporal cues within image models. This leads to reduced transferability of adversarial attacks from representation-enriched image models such as Supervised Vision Transformers (ViTs), Self-supervised ViTs (e.g., DINO), and Vision-language models (e.g., CLIP) to black-box video models. In this work, we induce dynamic cues within the image models without sacrificing their original performance on images. To this end, we optimize temporal prompts through frozen image models to capture motion dynamics. Our temporal prompts are the result of a learnable transformation that allows optimizing for temporal gradients during an adversarial attack to fool the motion dynamics. Specifically, we introduce spatial (image) and temporal (video) cues within the same source model through taskspecific prompts. Attacking such prompts maximizes the adversarial transferability from image-to-video and image-to-image models using the attacks designed for image models. As an example, an iterative attack launched from image model Deit-B with temporal prompts reduces generalization (top1 % accuracy) of a video model by 35% on Kinetics-400. Our approach also improves adversarial transferability to image models by 9% on ImageNet w.r.t the current state-of-the-art approach. Our attack results indicate that the attacker does not need specialized architectures, e.g., divided space-time attention, 3D convolutions, or multi-view convolution networks for different data modalities. Image models are effective surrogates to optimize an adversarial attack to fool black-box models in a changing environment over time. Code is available at https://bit.ly/3Xd9gRQ arXiv:2302.12252v2 [cs.CV] 4 Apr 2023Published as a conference paper at ICLR 2023 better to video-domain models. However, the image models lack dynamic temporal cues which are essential for transfer to the video models.We are motivated by the fact that in a real-world setting, a scene is not static but mostly involves various dynamics, e.g., object motion, changing viewpoints, illumination and background changes. Therefore, exploiting dynamic cues within an adversarial attack is essential to find blind-spots of unknown target models. For this purpose, we introduce the idea of encoding disentangled temporal representations within an image-based Vision Transformer (ViT) model using dedicated temporal prompts while keeping the remaining network frozen. The temporal prompts can learn the dynamic cues which are exploited during attack for improved transferability from image-domain models. Specifically, we introduce the proposed temporal prompts to three types of image models with enriched representations acquired via supervised (ViT (Dosovitskiy et al., 2020)), self-supervised (DINO (Caron et al., 2021)) or multi-modal learning (CLIP (Radford et al., 2021)).Our approach offers the benefit that the attacks do not need to rely on specialized networks designed for videos towards better adversarial transferability. As an example, popular model designs for videos incorporate 3D convolutions, space-time attention, tube embeddings or multi-view information to be robust against the temporal changes(Bertasius et al., 2021;Arnab et al., 2021). Without access to such specific design choices, our approach demonstrates how an attacker can leverage regular image models augmented with temporal prompts to learn dynamic cues. Further, our approach can be easily extended to image datasets, where disentangled representations can be learned via tokens across a scale-space at varying image resolutions. In summary, the major contributions of this work include: | Published as a conference paper at ICLR 2023 BOOSTING ADVERSARIAL TRANSFERABILITY USING DYNAMIC CUES |
d231855369 | We consider representation learning of 3D molecular graphs in which each atom is associated with a spatial position in 3D. This is an under-explored area of research, and a principled message passing framework is currently lacking. In this work, we conduct analyses in the spherical coordinate system (SCS) for the complete identification of 3D graph structures. Based on such observations, we propose the spherical message passing (SMP) as a novel and powerful scheme for 3D molecular learning. SMP dramatically reduces training complexity, enabling it to perform efficiently on large-scale molecules. In addition, SMP is capable of distinguishing almost all molecular structures, and the uncovered cases may not exist in practice. Based on meaningful physically-based representations of 3D information, we further propose the SphereNet for 3D molecular learning. Experimental results demonstrate that the use of meaningful 3D information in SphereNet leads to significant performance improvements in prediction tasks. Our results also demonstrate the advantages of SphereNet in terms of capability, efficiency, and scalability. Our code is publicly available as part of the DIG library | Published as a conference paper at ICLR 2022 SPHERICAL MESSAGE PASSING FOR 3D MOLECULAR GRAPHS |
d211252650 | We introduce the notion of property signatures, a representation for programs and program specifications meant for consumption by machine learning algorithms. Given a function with input type τ in and output type τ out , a property is a function of type: (τ in , τ out ) → Bool that (informally) describes some simple property of the function under consideration. For instance, if τ in and τ out are both lists of the same type, one property might ask 'is the input list the same length as the output list?'. If we have a list of such properties, we can evaluate them all for our function to get a list of outputs that we will call the property signature. Crucially, we can 'guess' the property signature for a function given only a set of input/output pairs meant to specify that function. We discuss several potential applications of property signatures and show experimentally that they can be used to improve over a baseline synthesizer so that it emits twice as many programs in less than one-tenth of the time. | Published as a conference paper at ICLR 2020 LEARNING TO REPRESENT PROGRAMS WITH PROPERTY SIGNATURES |
d213488539 | In many applications labeled data is not readily available, and needs to be collected via pain-staking human supervision. We propose a rule-exemplar method for collecting human supervision to combine the efficiency of rules with the quality of instance labels. The supervision is coupled such that it is both natural for humans and synergistic for learning. We propose a training algorithm that jointly denoises rules via latent coverage variables, and trains the model through a soft implication loss over the coverage and label variables. The denoised rules and trained model are used jointly for inference. Empirical evaluation on five different tasks shows that (1) our algorithm is more accurate than several existing methods of learning from a mix of clean and noisy supervision, and (2) the coupled rule-exemplar supervision is effective in denoising rules. | Published as a conference paper at ICLR 2020 LEARNING FROM RULES GENERALIZING LABELED EXEMPLARS |
d245906266 | It is a challenging task to learn rich and multi-scale spatiotemporal semantics from high-dimensional videos, due to large local redundancy and complex global dependency between video frames. The recent advances in this research have been mainly driven by 3D convolutional neural networks and vision transformers. Although 3D convolution can efficiently aggregate local context to suppress local redundancy from a small 3D neighborhood, it lacks the capability to capture global dependency because of the limited receptive field. Alternatively, vision transformers can effectively capture long-range dependency by self-attention mechanism, while having the limitation on reducing local redundancy with blind similarity comparison among all the tokens in each layer. Based on these observations, we propose a novel Unified transFormer (UniFormer) which seamlessly integrates merits of 3D convolution and spatiotemporal self-attention in a concise transformer format, and achieves a preferable balance between computation and accuracy. Different from traditional transformers, our relation aggregator can tackle both spatiotemporal redundancy and dependency, by learning local and global token affinity respectively in shallow and deep layers. We conduct extensive experiments on the popular video benchmarks, e.g., Kinetics-400, Kinetics-600, and Something-Something V1&V2. With only ImageNet-1K pretraining, our UniFormer achieves 82.9%/84.8% top-1 accuracy on Kinetics-400/Kinetics-600, while requiring 10× fewer GFLOPs than other state-of-the-art methods. For Something-Something V1 and V2, our UniFormer achieves new state-of-the-art performances of 60.9% and 71.2% top-1 accuracy respectively. | UNIFORMER: UNIFIED TRANSFORMER FOR EFFICIENT SPATIOTEMPORAL REPRESENTATION LEARNING |
d5334223 | Retrosynthesis is a technique to plan the chemical synthesis of organic molecules, for example drugs, agro-and fine chemicals. In retrosynthesis, a search tree is built by analysing molecules recursively and dissecting them into simpler molecular building blocks until one obtains a set of known building blocks. The search space is intractably large, and it is difficult to determine the value of retrosynthetic positions. Here, we propose to model retrosynthesis as a Markov Decision Process. In combination with a Deep Neural Network policy learned from essentially the complete published knowledge of chemistry, Monte Carlo Tree Search (MCTS) can be used to evaluate positions. In exploratory studies, we demonstrate that MCTS with neural network policies outperforms the traditionally used best-first search with hand-coded heuristics. | TOWARDS "ALPHACHEM": CHEMICAL SYNTHESIS PLANNING WITH TREE SEARCH AND DEEP NEURAL NETWORK POLICIES |
d256105572 | Representing a signal as a continuous function parameterized by neural network (a.k.a. Implicit Neural Representations, INRs) has attracted increasing attention in recent years. Neural Processes (NPs), which model the distributions over functions conditioned on partial observations (context set), provide a practical solution for fast inference of continuous functions. However, existing NP architectures suffer from inferior modeling capability for complex signals. In this paper, we propose an efficient NP framework dubbed Versatile Neural Processes (VNP), which largely increases the capability of approximating functions. Specifically, we introduce a bottleneck encoder that produces fewer and informative context tokens, relieving the high computational cost while providing high modeling capability. At the decoder side, we hierarchically learn multiple global latent variables that jointly model the global structure and the uncertainty of a function, enabling our model to capture the distribution of complex signals. We demonstrate the effectiveness of the proposed VNP on a variety of tasks involving 1D, 2D and 3D signals. Particularly, our method shows promise in learning accurate INRs w.r.t. a 3D scene without further finetuning. Code is available here. | Published as a conference paper at ICLR 2023 VERSATILE NEURAL PROCESSES FOR LEARNING IM- PLICIT NEURAL REPRESENTATIONS |
d257232713 | Reinforcement learning (RL) agents can leverage batches of previously collected data to extract a reasonable control policy. An emerging issue in this offline RL setting, however, is that the bootstrapping update underlying many of our methods suffers from insufficient action-coverage: standard max operator may select a maximal action that has not been seen in the dataset. Bootstrapping from these inaccurate values can lead to overestimation and even divergence. There are a growing number of methods that attempt to approximate an in-sample max, that only uses actions well-covered by the dataset. We highlight a simple fact: it is more straightforward to approximate an in-sample softmax using only actions in the dataset. We show that policy iteration based on the in-sample softmax converges, and that for decreasing temperatures it approaches the in-sample max. We derive an In-Sample Actor-Critic (AC), using this in-sample softmax, and show that it is consistently better or comparable to existing offline RL methods, and is also wellsuited to fine-tuning. We release the code at github.com/hwang-ua/inac pytorch. | Published as a conference paper at ICLR 2023 THE IN-SAMPLE SOFTMAX FOR OFFLINE REINFORCE- MENT LEARNING |
d3502463 | Recent advances in deep reinforcement learning have made significant strides in performance on applications such as Go and Atari games. However, developing practical methods to balance exploration and exploitation in complex domains remains largely unsolved. Thompson Sampling and its extension to reinforcement learning provide an elegant approach to exploration that only requires access to posterior samples of the model. At the same time, advances in approximate Bayesian methods have made posterior approximation for flexible neural network models practical. Thus, it is attractive to consider approximate Bayesian neural networks in a Thompson Sampling framework. To understand the impact of using an approximate posterior on Thompson Sampling, we benchmark well-established and recently developed methods for approximate posterior sampling combined with Thompson Sampling over a series of contextual bandit problems. We found that many approaches that have been successful in the supervised learning setting underperformed in the sequential decision-making scenario. In particular, we highlight the challenge of adapting slowly converging uncertainty estimates to the online setting. * Google AI Resident Published as a conference paper at ICLR 2018 1: Input: Prior distribution over models, π 0 : θ ∈ Θ → [0, 1]. 2: for time t = 0, . . . , N do 3:Observe context X t ∈ R d .4:Sample model θ t ∼ π t .5:Compute a t = BestAction(X t , θ t ).6:Select action a t and observe reward r t .7:Update posterior distribution π t+1 with (X t , a t , r t ).In the following sections we rely on the idea that, if we had access to the actual posterior π t given the observed data at all times t, then choosing actions using Thompson Sampling would lead to near-optimal cumulative regret or, more informally, to good performance. It is important to remark that in some problems this is not necessarily the case; for example, when actions that have no chance of being optimal still convey useful information about other actions. Thompson Sampling (or UCB approaches) would never select such actions, even if they are worth their cost (Russo & Van Roy, 2014). In addition, Thompson Sampling does not take into account the time horizon where the process ends, and if known, exploration efforts should be tuned accordingly (Russo et al., 2017). Nonetheless, under the assumption that very accurate posterior approximations lead to efficient decisions, the question is: what happens when the approximations are not so accurate? In some cases, the mismatch in posteriors may not hurt in terms of decision making, and we will still end up with good decisions. Unfortunately, in other cases, this mismatch together with its induced feedback loop will degenerate in a significant loss of performance. We would like to understand the main aspects that determine which way it goes. This is an important practical question as, in large and complex systems, computational sacrifices and statistical assumptions are made to favor simplicity and tractability. But, what is their impact? | Published as a conference paper at ICLR 2018 DEEP BAYESIAN BANDITS SHOWDOWN AN EMPIRICAL COMPARISON OF BAYESIAN DEEP NETWORKS FOR THOMPSON SAMPLING |
d252762466 | Systems neuroscience relies on two complementary views of neural data, characterized by single neuron tuning curves and analysis of population activity. These two perspectives combine elegantly in neural latent variable models that constrain the relationship between latent variables and neural activity, modeled by simple tuning curve functions. This has recently been demonstrated using Gaussian processes, with applications to realistic and topologically relevant latent manifolds. Those and previous models, however, missed crucial shared coding properties of neural populations. We propose feature sharing across neural tuning curves which significantly improves performance and helps optimization. We also propose a solution to the ensemble detection problem, where different groups of neurons, i.e., ensembles, can be modulated by different latent manifolds. Achieved through a soft clustering of neurons during training, this allows for the separation of mixed neural populations in an unsupervised manner. These innovations lead to more interpretable models of neural population activity that train well and perform better even on mixtures of complex latent manifolds. Finally, we apply our method on a recently published grid cell dataset, and recover distinct ensembles, infer toroidal latents and predict neural tuning curves in a single integrated modeling framework. dogenous sequential cortical activity evoked by visual stimuli. Journal of neuroscience, 35(23): 8813-8828, 2015. Rishidev Chaudhuri, Berk Gerçek, Biraj Pandey, Adrien Peyrache, and Ila Fiete. The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep. Nature neuroscience, 22(9):1512-1520, 2019. state tunes mouse vision to ethological features through pupil dilation. bioRxiv, 2021. Mark C Fuhs and David S Touretzky. A spin glass model of path integration in rat medial entorhinal cortex. Resisting adversarial attacks using gaussian mixture variational autoencoders. In Ulisse Ferrari, and Olivier Marre. Context-dependent selectivity to natural scenes in the retina. bioRxiv, 2021. Torkel Hafting, Marianne Fyhn, Sturla Molden, May-Britt Moser, and Edvard I Moser. Microstructure of a spatial map in the entorhinal cortex. Nature, 436(7052):801-806, 2005. multiplexed, heterogeneous, and adaptive code for navigation in medial entorhinal cortex. Neuron, 94(2): 375-387, 2017. Christopher D Harvey, Philip Coen, and David W Tank. Choice-specific sequences in parietal cortex during a virtual-navigation decision task. Nature, 484(7392):62-68, 2012. activity in the null space: permitting preparation without movement. Nature neuroscience, 17(3):440-448, 2014. Alexander JE Kell and Josh H McDermott. Deep neural network models of sensory systems: windows onto the role of task constraints. Current opinion in neurobiology, 55:121-132, 2019. | Published as a conference paper at ICLR 2023 UNDERSTANDING NEURAL CODING ON LATENT MAN- IFOLDS BY SHARING FEATURES AND DIVIDING EN- SEMBLES |
d246867402 | Recently Graph Injection Attack (GIA) emerges as a practical attack scenario on Graph Neural Networks (GNNs), where the adversary can merely inject few malicious nodes instead of modifying existing nodes or edges, i.e., Graph Modification Attack (GMA). Although GIA has achieved promising results, little is known about why it is successful and whether there is any pitfall behind the success. To understand the power of GIA, we compare it with GMA and find that GIA can be provably more harmful than GMA due to its relatively high flexibility. However, the high flexibility will also lead to great damage to the homophily distribution of the original graph, i.e., similarity among neighbors. Consequently, the threats of GIA can be easily alleviated or even prevented by homophily-based defenses designed to recover the original homophily. To mitigate the issue, we introduce a novel constraint -homophily unnoticeability that enforces GIA to preserve the homophily, and propose Harmonious Adversarial Objective (HAO) to instantiate it. Extensive experiments verify that GIA with HAO can break homophily-based defenses and outperform previous GIA attacks by a significant margin. We believe our methods can serve for a more reliable evaluation of the robustness of GNNs. | Published as a conference paper at ICLR 2022 UNDERSTANDING AND IMPROVING GRAPH INJECTION ATTACK BY PROMOTING UNNOTICEABILITY |
d2350854 | A key requirement for the development of effective learning representations is their evaluation and comparison to representations we know to be effective. In natural sensory domains, the community has viewed the brain as a source of inspiration and as an implicit benchmark for success. However, it has not been possible to directly test representational learning algorithms directly against the representations contained in neural systems. Here, we propose a new benchmark for visual representations on which we have directly tested the neural representation in multiple visual cortical areas in macaque (utilizing data from [Majaj et al., 2012]), and on which any computer vision algorithm that produces a feature space can be tested. The benchmark measures the effectiveness of the neural or machine representation by computing the classification loss on the ordered eigendecomposition of a kernel matrix [Montavon et al., 2011]. In our analysis we find that the neural representation in visual area IT is superior to visual area V4, indicating an increase in representational performance in higher levels of the cortical visual hierarchy. In our analysis of representational learning algorithms, we find that three-layer models approach the representational performance of V4 and the algorithm in [Le et al., 2012] surpasses the performance of V4. Impressively, we find that a recent supervised algorithm [Krizhevsky et al., 2012] achieves performance comparable to that of IT for an intermediate level of image variation difficulty, and surpasses IT at a higher difficulty level. We believe this result represents a major milestone: it is the first learning algorithm we have found that exceeds our current estimate of IT representation performance. To enable researchers to utilize this benchmark, we make available image datasets, analysis tools, and neural measurements of V4 and IT. We hope that this benchmark will assist the community in matching the representational performance of visual cortex and will serve as an initial rallying point for further correspondence between representations derived in brains and machines. | The Neural Representation Benchmark and its Evaluation on Brain and Machine |
d256808600 | Implicit Neural Representations (INRs) have emerged in the last few years as a powerful tool to encode continuously a variety of different signals like images, videos, audio and 3D shapes. When applied to 3D shapes, INRs allow to overcome the fragmentation and shortcomings of the popular discrete representations used so far. Yet, considering that INRs consist in neural networks, it is not clear whether and how it may be possible to feed them into deep learning pipelines aimed at solving a downstream task. In this paper, we put forward this research problem and propose inr2vec, a framework that can compute a compact latent representation for an input INR in a single inference pass. We verify that inr2vec can embed effectively the 3D shapes represented by the input INRs and show how the produced embeddings can be fed into deep learning pipelines to solve several tasks by processing exclusively INRs. | Published as a conference paper at ICLR 2023 DEEP LEARNING ON IMPLICIT NEURAL REPRESENTATIONS OF SHAPES |
d222134189 | Convolutional neural networks (CNN) exhibit unmatched performance in a multitude of computer vision tasks. However, the advantage of using convolutional networks over fully-connected networks is not understood from a theoretical perspective. In this work, we show how convolutional networks can leverage locality in the data, and thus achieve a computational advantage over fully-connected networks. Specifically, we show a class of problems that can be efficiently solved using convolutional networks trained with gradient-descent, but at the same time is hard to learn using a polynomial-size fully-connected network. | COMPUTATIONAL SEPARATION BETWEEN CONVOLU- TIONAL AND FULLY-CONNECTED NETWORKS |
d248084993 | Multiple views of data, both naturally acquired (e.g., image and audio) and artificially produced (e.g., via adding different noise to data samples), have proven useful in enhancing representation learning. Natural views are often handled by multiview analysis tools, e.g., (deep) canonical correlation analysis [(D)CCA], while the artificial ones are frequently used in self-supervised learning (SSL) paradigms, e.g., BYOL and Barlow Twins. Both types of approaches often involve learning neural feature extractors such that the embeddings of data exhibit high cross-view correlations. Although intuitive, the effectiveness of correlationbased neural embedding is mostly empirically validated. This work aims to understand latent correlation maximization-based deep multiview learning from a latent component identification viewpoint. An intuitive generative model of multiview data is adopted, where the views are different nonlinear mixtures of shared and private components. Since the shared components are view/distortion-invariant, representing the data using such components is believed to reveal the identity of the samples effectively and robustly. Under this model, latent correlation maximization is shown to guarantee the extraction of the shared components across views (up to certain ambiguities). In addition, it is further shown that the private information in each view can be provably disentangled from the shared using proper regularization design. A finite sample analysis, which has been rare in nonlinear mixture identifiability study, is also presented. The theoretical results and newly designed regularization are tested on a series of tasks.Published as a conference paper at ICLR 2022Notably, many DCCA and AM-SSL approaches involve (explicitly or implicitly) searching for highly correlated representations from multiple views, using neural feature extractors (encoders). The empirical success of DCCA and AM-SSL bears an important research question: How to understand the role of cross-view correlation in deep multiview learning? Furthermore, how to use such understanding to design theory-backed learning criteria to serve various purposes?The Jacobian of h (1) can be expressed using the following block form11 J (1) 12 J (1) 21 J (1) 22 , | Published as a conference paper at ICLR 2022 UNDERSTANDING LATENT CORRELATION-BASED MULTIVIEW LEARNING AND SELF-SUPERVISION: AN IDENTIFIABILITY PERSPECTIVE |
d259298198 | Errors in labels obtained via human annotation adversely affect a model's performance.Existing approaches propose ways to mitigate the effect of label error on a model's downstream accuracy, yet little is known about its impact on a model's disparity metrics 1 .Here we study the effect of label error on a model's disparity metrics.We empirically characterize how varying levels of label error, in both training and test data, affect these disparity metrics.We find that group calibration and other metrics are sensitive to train-time and test-time label error-particularly for minority groups.This disparate effect persists even for models trained with noise-aware algorithms.To mitigate the impact of training-time label error, we present an approach to estimate the influence of a training input's label on a model's group disparity metric.We empirically assess the proposed approach on a variety of datasets and find significant improvement, compared to alternative approaches, in identifying training inputs that improve a model's disparity metric.We complement the approach with an automatic relabel-and-finetune scheme that produces updated models with, provably, improved group calibration error. | QUANTIFYING AND MITIGATING THE IMPACT OF LA-BEL ERRORS ON MODEL DISPARITY METRICS |
d8875939 | The quality of data representation in deep learning methods is directly related to the prior model imposed on the representations; however, generally used fixed priors are not capable of adjusting to the context in the data. To address this issue, we propose deep predictive coding networks, a hierarchical generative model that empirically alters priors on the latent representations in a dynamic and contextsensitive manner. This model captures the temporal dependencies in time-varying signals and uses top-down information to modulate the representation in lower layers. The centerpiece of our model is a novel procedure to infer sparse states of a dynamic network which is used for feature extraction. We also extend this feature extraction block to introduce a pooling function that captures locally invariant representations. When applied on a natural video data, we show that our method is able to learn high-level visual features. We also demonstrate the role of the topdown connections by showing the robustness of the proposed model to structured noise. | Deep Predictive Coding Networks |
d201058750 | It is well-known that overparametrized neural networks trained using gradientbased methods quickly achieve small training error with appropriate hyperparameter settings. Recent papers have proved this statement theoretically for highly overparametrized networks under reasonable assumptions. These results either assume that the activation function is ReLU or they depend on the minimum eigenvalue of a certain Gram matrix. In the latter case, existing works only prove that this minimum eigenvalue is non-zero and do not provide quantitative bounds which require that this eigenvalue be large. Empirically, a number of alternative activation functions have been proposed which tend to perform better than ReLU at least in some settings but no clear understanding has emerged. This state of affairs underscores the importance of theoretically understanding the impact of activation functions on training. In the present paper, we provide theoretical results about the effect of activation function on the training of highly overparametrized 2-layer neural networks. A crucial property that governs the performance of an activation is whether or not it is smooth:• For non-smooth activations such as ReLU, SELU, ELU, which are not smooth because there is a point where either the first order or second order derivative is discontinuous, all eigenvalues of the associated Gram matrix are large under minimal assumptions on the data. • For smooth activations such as tanh, swish, polynomial, which have derivatives of all orders at all points, the situation is more complex: if the subspace spanned by the data has small dimension then the minimum eigenvalue of the Gram matrix can be small leading to slow training. But if the dimension is large and the data satisfies another mild condition, then the eigenvalues are large. If we allow deep networks, then the small data dimension is not a limitation provided that the depth is sufficient.We discuss a number of extensions and applications of these results. 1 | EFFECT OF ACTIVATION FUNCTIONS ON THE TRAIN- ING OF OVERPARAMETRIZED NEURAL NETS |
d212802994 | Stochastic gradient descent (SGD) with stochastic momentum is popular in nonconvex stochastic optimization and particularly for the training of deep neural networks. In standard SGD, parameters are updated by improving along the path of the gradient at the current iterate on a batch of examples, where the addition of a "momentum" term biases the update in the direction of the previous change in parameters. In non-stochastic convex optimization one can show that a momentum adjustment provably reduces convergence time in many settings, yet such results have been elusive in the stochastic and non-convex settings. At the same time, a widely-observed empirical phenomenon is that in training deep networks stochastic momentum appears to significantly improve convergence time, variants of it have flourished in the development of other popular update methods, e.g. ADAM (Kingma & Ba(2015)), AMSGrad (Reddi et al. (2018b)), etc. Yet theoretical justification for the use of stochastic momentum has remained a significant open question. In this paper we propose an answer: stochastic momentum improves deep network training because it modifies SGD to escape saddle points faster and, consequently, to more quickly find a second order stationary point. Our theoretical results also shed light on the related question of how to choose the ideal momentum parameter-our analysis suggests that β ∈ [0, 1) should be large (close to 1), which comports with empirical findings. We also provide experimental findings that further validate these conclusions. | Published as a conference paper at ICLR 2020 ESCAPING SADDLE POINTS FASTER WITH STOCHASTIC MOMENTUM |
d258426655 | We propose Structured Exploration with Achievements (SEA), a multi-stage reinforcement learning algorithm designed for achievement-based environments, a particular type of environment with an internal achievement set. SEA first uses offline data to learn a representation of the known achievements with a determinant loss function, then recovers the dependency graph of the learned achievements with a heuristic algorithm, and finally interacts with the environment online to learn policies that master known achievements and explore new ones with a controller built with the recovered dependency graph. We empirically demonstrate that SEA can recover the achievement structure accurately and improve exploration in hard domains such as Crafter that are procedurally generated with highdimensional observations like images. | Published as a conference paper at ICLR 2023 LEARNING ACHIEVEMENT STRUCTURE FOR STRUC- TURED EXPLORATION IN DOMAINS WITH SPARSE RE- WARD |
d255341108 | Can we build continuous generative models which generalize across scales, can be evaluated at any coordinate, admit calculation of exact derivatives, and are conceptually simple? Existing MLP-based architectures generate worse samples than the grid-based generators with favorable convolutional inductive biases. Models that focus on generating images at different scales do better, but employ complex architectures not designed for continuous evaluation of images and derivatives. We take a signal-processing perspective and treat continuous image generation as interpolation from samples. Indeed, correctly sampled discrete images contain all information about the low spatial frequencies. The question is then how to extrapolate the spectrum in a data-driven way while meeting the above design criteria. Our answer is FunkNN-a new convolutional network which learns how to reconstruct continuous images at arbitrary coordinates and can be applied to any image dataset. Combined with a discrete generative model it becomes a functional generator which can act as a prior in continuous ill-posed inverse problems. We show that FunkNN generates high-quality continuous images and exhibits strong out-of-distribution performance thanks to its patch-based design. We further showcase its performance in several stylized inverse problems with exact spatial derivatives. Our implementation is available at https://github.com/swing-research/FunkNN. | Published as a conference paper at ICLR 2023 FUNKNN: NEURAL INTERPOLATION FOR FUNCTIONAL GENERATION |
d211096737 | We tackle the problem of discovering novel classes in an image collection given labelled examples of other classes. This setting is similar to semi-supervised learning, but significantly harder because there are no labelled examples for the new classes. The challenge, then, is to leverage the information contained in the labelled images in order to learn a general-purpose clustering model and use the latter to identify the new classes in the unlabelled data. In this work we address this problem by combining three ideas: (1) we suggest that the common approach of bootstrapping an image representation using the labeled data only introduces an unwanted bias, and that this can be avoided by using self-supervised learning to train the representation from scratch on the union of labelled and unlabelled data; (2) we use rank statistics to transfer the model's knowledge of the labelled classes to the problem of clustering the unlabelled images; and, (3) we train the data representation by optimizing a joint objective function on the labelled and unlabelled subsets of the data, improving both the supervised classification of the labelled data, and the clustering of the unlabelled data. We evaluate our approach on standard classification benchmarks and outperform current methods for novel category discovery by a significant margin. * indicates equal contribution | Published as a conference paper at ICLR 2020 AUTOMATICALLY DISCOVERING AND LEARNING NEW VISUAL CATEGORIES WITH RANKING STATISTICS |
d247475824 | A recent line of work on black-box adversarial attacks has revived the use of transfer from surrogate models by integrating it into query-based search. However, we find that existing approaches of this type underperform their potential, and can be overly complicated besides. Here, we provide a short and simple algorithm which achieves state-of-the-art results through a search which uses the surrogate network's classscore gradients, with no need for other priors or heuristics. The guiding assumption of the algorithm is that the studied networks are in a fundamental sense learning similar functions, and that a transfer attack from one to the other should thus be fairly "easy". This assumption is validated by the extremely low query counts and failure rates achieved: e.g. an untargeted attack on a VGG-16 ImageNet network using a ResNet-152 as the surrogate yields a median query count of 6 at a success rate of 99.9%. Code is available at https | Published as a conference paper at ICLR 2022 ATTACKING DEEP NETWORKS WITH SURROGATE- BASED ADVERSARIAL BLACK-BOX METHODS IS EASY |
d222208985 | This paper studies learning logic rules for reasoning on knowledge graphs. Logic rules provide interpretable explanations when used for prediction as well as being able to generalize to other tasks, and hence are critical to learn. Existing methods either suffer from the problem of searching in a large search space (e.g., neural logic programming) or ineffective optimization due to sparse rewards (e.g., techniques based on reinforcement learning). To address these limitations, this paper proposes a probabilistic model called RNNLogic. RNNLogic treats logic rules as a latent variable, and simultaneously trains a rule generator as well as a reasoning predictor with logic rules. We develop an EM-based algorithm for optimization. In each iteration, the reasoning predictor is first updated to explore some generated logic rules for reasoning. Then in the E-step, we select a set of high-quality rules from all generated rules with both the rule generator and reasoning predictor via posterior inference; and in the M-step, the rule generator is updated with the rules selected in the E-step. Experiments on four datasets prove the effectiveness of RNNLogic. * Equal contribution.In this paper, we propose a principled probabilistic approach called RNNLogic which overcomes the above limitations. Our approach consists of a rule generator as well as a reasoning predictor with logic rules, which are simultaneously trained to enhance each other. The rule generator provides logic rules which are used by the reasoning predictor for reasoning, while the reasoning predictor provides effective reward to train the rule generator, which helps significantly reduce the search space. Specifically, for each query-answer pair, e.g., q = (h, r, ?) and a = t, we model the probability of the answer conditioned on query and existing knowledge graph G, i.e., p(a|G, q), where a set of logic rules z 1 is treated as a latent variable. The rule generator defines a prior distribution over logic rules for each query, i.e., p(z|q), which is parameterized by a recurrent neural network. The reasoning predictor computes the likelihood of the answer conditioned on the logic rules and the existing knowledge graph G, i.e., p(a|G, q, z). At each training iteration, we first sample a few logic rules from the rule generator, and further update the reasoning predictor to try out these rules for prediction. Then an EM algorithm (Neal & Hinton, 1998) is used to optimize the rule generator. In the E-step, a set of high-quality logic rules are selected from all the generated rules according to their posterior probabilities. In the M-step, the rule generator is updated to imitate the high-quality rules selected in the E-step. Extensive experimental results show that RNNLogic outperforms state-of-the-art methods for knowledge graph reasoning 2 . Besides, RNNLogic is able to generate high-quality logic rules. | Published as a conference paper at ICLR 2021 RNNLOGIC: LEARNING LOGIC RULES FOR REASON- ING ON KNOWLEDGE GRAPHS |
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