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d182953182 | A key component of most neural network architectures is the use of normalization layers, such as Batch Normalization. Despite its common use and large utility in optimizing deep architectures, it has been challenging both to generically improve upon Batch Normalization and to understand the circumstances that lend themselves to other enhancements. In this paper, we identify four improvements to the generic form of Batch Normalization and the circumstances under which they work, yielding performance gains across all batch sizes while requiring no additional computation during training. These contributions include proposing a method for reasoning about the current example in inference normalization statistics, fixing a training vs. inference discrepancy; recognizing and validating the powerful regularization effect of Ghost Batch Normalization for small and medium batch sizes; examining the effect of weight decay regularization on the scaling and shifting parameters γ and β; and identifying a new normalization algorithm for very small batch sizes by combining the strengths of Batch and Group Normalization. We validate our results empirically on six datasets: CIFAR-100, SVHN, Caltech-256, Oxford Flowers-102, CUB-2011, and ImageNet. | Published as a conference paper at ICLR 2020 FOUR THINGS EVERYONE SHOULD KNOW TO IMPROVE BATCH NORMALIZATION |
d257219743 | Tackling unfairness in graph learning models is a challenging task, as the unfairness issues on graphs involve both attributes and topological structures. Existing work on fair graph learning simply assumes that attributes of all nodes are available for model training and then makes fair predictions. In practice, however, the attributes of some nodes might not be accessible due to missing data or privacy concerns, which makes fair graph learning even more challenging. In this paper, we propose FairAC, a fair attribute completion method, to complement missing information and learn fair node embeddings for graphs with missing attributes. FairAC adopts an attention mechanism to deal with the attribute missing problem and meanwhile, it mitigates two types of unfairness, i.e., feature unfairness from attributes and topological unfairness due to attribute completion. FairAC can work on various types of homogeneous graphs and generate fair embeddings for them and thus can be applied to most downstream tasks to improve their fairness performance. To our best knowledge, FairAC is the first method that jointly addresses the graph attribution completion and graph unfairness problems. Experimental results on benchmark datasets show that our method achieves better fairness performance with less sacrifice in accuracy, compared with the state-of-the-art methods of fair graph learning. | Published as a conference paper at ICLR 2023 FAIR ATTRIBUTE COMPLETION ON GRAPH WITH MISSING ATTRIBUTES |
d244130146 | Reconstructing medical images from partial measurements is an important inverse problem in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Existing solutions based on machine learning typically train a model to directly map measurements to medical images, leveraging a training dataset of paired images and measurements. These measurements are typically synthesized from images using a fixed physical model of the measurement process, which hinders the generalization capability of models to unknown measurement processes. To address this issue, we propose a fully unsupervised technique for inverse problem solving, leveraging the recently introduced score-based generative models. Specifically, we first train a score-based generative model on medical images to capture their prior distribution. Given measurements and a physical model of the measurement process at test time, we introduce a sampling method to reconstruct an image consistent with both the prior and the observed measurements. Our method does not assume a fixed measurement process during training, and can thus be flexibly adapted to different measurement processes at test time. Empirically, we observe comparable or better performance to supervised learning techniques in several medical imaging tasks in CT and MRI, while demonstrating significantly better generalization to unknown measurement processes. | Published as a conference paper at ICLR 2022 SOLVING INVERSE PROBLEMS IN MEDICAL IMAGING WITH SCORE-BASED GENERATIVE MODELS |
d3464537 | Deep latent variable models are powerful tools for representation learning. In this paper, we adopt the deep information bottleneck model, identify its shortcomings and propose a model that circumvents them. To this end, we apply a copula transformation which, by restoring the invariance properties of the information bottleneck method, leads to disentanglement of the features in the latent space. Building on that, we show how this transformation translates to sparsity of the latent space in the new model. We evaluate our method on artificial and real data.Published as a conference paper at ICLR 2018 of the information bottleneck, proposed by(Rey et al., 2014). It translates to a more compact latent space that results in a better interpretability of the model. | Published as a conference paper at ICLR 2018 LEARNING SPARSE LATENT REPRESENTATIONS WITH THE DEEP COPULA INFORMATION BOTTLENECK |
d257833684 | Contrastive learning methods train visual encoders by comparing views (e.g., often created via a group of data augmentations on the same instance) from one instance to others. Typically, the views created from one instance are set as positive, while views from other instances are negative. This binary instance discrimination is studied extensively to improve feature representations in self-supervised learning. In this paper, we rethink the instance discrimination framework and find the binary instance labeling insufficient to measure correlations between different samples. For an intuitive example, given a random image instance, there may exist other images in a mini-batch whose content meanings are the same (i.e., belonging to the same category) or partially related (i.e., belonging to a similar category). How to treat the images that correlate similarly to the current image instance leaves an unexplored problem. We thus propose to support the current image by exploring other correlated instances (i.e., soft neighbors). We first carefully cultivate a candidate neighbor set, which will be further utilized to explore the highly-correlated instances. A cross-attention module is then introduced to predict the correlation score (denoted as positiveness) of other correlated instances with respect to the current one. The positiveness score quantitatively measures the positive support from each correlated instance, and is encoded into the objective for pretext training. To this end, our proposed method benefits in discriminating uncorrelated instances while absorbing correlated instances for SSL. We evaluate our soft neighbor contrastive learning method (SNCLR) on standard visual recognition benchmarks, including image classification, object detection, and instance segmentation. The state-of-theart recognition performance shows that SNCLR is effective in improving feature representations from both ViT and CNN encoders. * Corresponding author. We provide the homepage for this project. | Published as a conference paper at ICLR 2023 SOFT NEIGHBORS ARE POSITIVE SUPPORTERS IN CONTRASTIVE VISUAL REPRESENTATION LEARNING |
d252715605 | Clustering algorithms are widely used in many societal resource allocation applications, such as loan approvals and candidate recruitment, among others, and hence, biased or unfair model outputs can adversely impact individuals that rely on these applications. To this end, many fair clustering approaches have been recently proposed to counteract this issue. Due to the potential for significant harm, it is essential to ensure that fair clustering algorithms provide consistently fair outputs even under adversarial influence. However, fair clustering algorithms have not been studied from an adversarial attack perspective. In contrast to previous research, we seek to bridge this gap and conduct a robustness analysis against fair clustering by proposing a novel black-box fairness attack. Through comprehensive experiments 1 , we find that state-of-the-art models are highly susceptible to our attack as it can reduce their fairness performance significantly. Finally, we propose Consensus Fair Clustering (CFC), the first robust fair clustering approach that transforms consensus clustering into a fair graph partitioning problem, and iteratively learns to generate fair cluster outputs. Experimentally, we observe that CFC is highly robust to the proposed attack and is thus a truly robust fair clustering alternative.Published as a conference paper at ICLR 2023 been explored from an adversarial attack perspective, which leaves the whole area of unsupervised fair clustering in potential danger. This leads us to our fundamental research questions in this paper:Are fair clustering algorithms vulnerable to adversarial attacks that seek to decrease fairness utility, and if such attacks exist, how do we develop an adversarially robust fair clustering model?Contributions. In this paper, we answer both these questions in the affirmative by making the following contributions:• We propose a novel black-box adversarial attack against clustering models where the attacker can perturb a small percentage of protected group memberships and yet is able to degrade the fairness performance of state-of-the-art fair clustering models significantly (Section 2). We also discuss how our attack is critically different from existing adversarial attacks against clustering performance and why they cannot be used for the proposed threat model. • Through extensive experiments using our attack approach, we find that existing fair clustering algorithms are not robust to adversarial influence, and are extremely volatile with regards to fairness utility (Section 2.2). We conduct this analysis on a number of real-world datasets, and for a variety of clustering performance and fairness utility metrics. | Published as a conference paper at ICLR 2023 ROBUST FAIR CLUSTERING: A NOVEL FAIRNESS ATTACK AND DEFENSE FRAMEWORK |
d226282371 | The existing Neural ODE formulation relies on an explicit knowledge of the termination time. We extend Neural ODEs to implicitly defined termination criteria modeled by neural event functions, which can be chained together and differentiated through. Neural Event ODEs are capable of modeling discrete and instantaneous changes in a continuous-time system, without prior knowledge of when these changes should occur or how many such changes should exist. We test our approach in modeling hybrid discrete-and continuous-systems such as switching dynamical systems and collision in multi-body systems, and we propose simulation-based training of point processes with applications in discrete control. | Published as a conference paper at ICLR 2021 LEARNING NEURAL EVENT FUNCTIONS FOR ORDINARY DIFFERENTIAL EQUATIONS |
d249151922 | Graph neural networks (GNNs) continue to achieve state-of-the-art performance on many graph learning tasks, but rely on the assumption that a given graph is a sufficient approximation of the true neighborhood structure. When a system contains higher-order sequential dependencies, we show that the tendency of traditional graph representations to underfit each node's neighborhood causes existing GNNs to generalize poorly. To address this, we propose a novel Deep Graph Ensemble (DGE), which captures neighborhood variance by training an ensemble of GNNs on different neighborhood subspaces of the same node within a higherorder network representation. We show that DGE consistently outperforms existing GNNs on semisupervised and supervised tasks on six real-world data sets with known higher-order dependencies, even under a similar parameter budget. We demonstrate that diverse and accurate base classifiers are central to DGE's success, and discuss the implications of these findings for future work on ensembles of GNNs. . Raw-gnn: Random walk aggregation based graph neural network. arXiv preprint arXiv:2206.13953, 2022b.Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional net-, and Nitesh V Chawla. Higher-order networks of diabetes comorbidities: Disease trajectories that matter. . Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems, 30, 2017.Renaud Lambiotte, Martin Rosvall, and Ingo Scholtes. From networks to optimal higher-order models of complex systems. Nature physics, 15(4):313-320, 2019. | Published as a conference paper at ICLR 2023 DEEP ENSEMBLES FOR GRAPHS WITH HIGHER-ORDER DEPENDENCIES |
d232404824 | Can deep learning solve multiple tasks simultaneously, even when they are unrelated and very different? We investigate how the representations of the underlying tasks affect the ability of a single neural network to learn them jointly. We present theoretical and empirical findings that a single neural network is capable of simultaneously learning multiple tasks from a combined data set, for a variety of methods for representing tasks-for example, when the distinct tasks are encoded by well-separated clusters or decision trees over certain task-code attributes. More concretely, we present a novel analysis that shows that families of simple programming-like constructs for the codes encoding the tasks are learnable by two-layer neural networks with standard training. We study more generally how the complexity of learning such combined tasks grows with the complexity of the task codes; we find that combining many tasks may incur a sample complexity penalty, even though the individual tasks are easy to learn. We provide empirical support for the usefulness of the learning bounds by training networks on clusters, decision trees, and SQL-style aggregation. Uszkoreit. One model to learn them all. arXiv preprint arXiv:1706.05137, 2017.Michael Kearns. Efficient noise-tolerant learning from statistical queries. . Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538, 2017. and practical bert models for sequence labeling. arXiv preprint arXiv:1909.00100, 2019.Gregory Valiant. Finding correlations in subquadratic time, with applications to learning parities and the closest pair problem. survey on multi-task learning. arXiv preprint arXiv:1707.08114, 2017. | Published as a conference paper at ICLR 2021 ONE NETWORK FITS ALL? MODULAR VERSUS MONOLITHIC TASK FORMULATIONS IN NEURAL NETWORKS |
d12734615 | The policy gradients of the expected return objective can react slowly to rare rewards. Yet, in some cases agents may wish to emphasize the low or high returns regardless of their probability. Borrowing from the economics and control literature, we review the risk-sensitive value function that arises from an exponential utility and illustrate its effects on an example. This risk-sensitive value function is not always applicable to reinforcement learning problems, so we introduce the particle value function defined by a particle filter over the distributions of an agent's experience, which bounds the risk-sensitive one. We illustrate the benefit of the policy gradients of this objective in Cliffworld.Stefano Coraluppi. Optimal control of Markov decision processes for performance and robustness. . Risk-sensitive Markov decision processes. Management science, 18(7):356-369, 1972. Nikolas Kantas. Sequential decision making in general state space models. PhD thesis, Citeseer, 2009. Nikolas Kantas, Arnaud Doucet, Sumeetpal S Singh, Jan Maciejowski, Nicolas Chopin, et al. On particle methods for parameter estimation in state-space models. Statistical science, 30(3):328-351, 2015. Hilbert J Kappen. Linear theory for control of nonlinear stochastic systems. Physical review letters, 95(20):200201, 2005. Hilbert J Kappen, Vicenç Gómez, and Manfred Opper. Optimal control as a graphical model inference problem. Machine learning, 87(2):159-182, 2012. Sven Koenig and Reid G Simmons. Risk-sensitive planning with probabilistic decision graphs. In . On some properties of markov chain monte carlo simulation methods based on the particle filter. Journal of Econometrics, . Risk-sensitive reinforcement learning. Neural computation, 26(7):1298-1328, 2014. Naftali Tishby and Daniel Polani. Information theory of decisions and actions. In Perception-action cycle, pp. 601-636. Springer, 2011. Emanuel Todorov. Linearly-solvable markov decision problems. In NIPS, pp. 1369-1376, 2006. Marc Toussaint and Amos Storkey. Probabilistic inference for solving discrete and continuous state markov decision processes. In Proceedings of the 23rd international conference on Machine learning, pp. 945-952. ACM, 2006. John Von Neumann and Oskar Morgenstern. Theory of games and economic behavior. Princeton University Press, 1953. Ronald J Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning, 8(3-4):229-256, 1992. | Workshop track -ICLR 2017 PARTICLE VALUE FUNCTIONS |
d211021032 | The question of whether deep neural networks are good at generalising beyond their immediate training experience is of critical importance for learning-based approaches to AI. Here, we consider tests of out-of-sample generalisation that require an agent to respond to never-seen-before instructions by manipulating and positioning objects in a 3D Unity simulated room. We first describe a comparatively generic agent architecture that exhibits strong performance on these tests. We then identify three aspects of the training regime and environment that make a significant difference to its performance: (a) the number of object/word experiences in the training set; (b) the visual invariances afforded by the agent's perspective, or frame of reference; and (c) the variety of visual input inherent in the perceptual aspect of the agent's perception. Our findings indicate that the degree of generalisation that networks exhibit can depend critically on particulars of the environment in which a given task is instantiated. They further suggest that the propensity for neural networks to generalise in systematic ways may increase if, like human children, those networks have access to many frames of richly varying, multi-modal observations as they learn. * Work carried out during internship at DeepMind. | ENVIRONMENTAL DRIVERS OF SYSTEMATICITY AND GENERALISATION IN A SITUATED AGENT |
d233024779 | Though convolutional neural networks (CNNs) have demonstrated remarkable ability in learning discriminative features, they often generalize poorly to unseen domains. Domain generalization aims to address this problem by learning from a set of source domains a model that is generalizable to any unseen domain. In this paper, a novel approach is proposed based on probabilistically mixing instancelevel feature statistics of training samples across source domains. Our method, termed MixStyle, is motivated by the observation that visual domain is closely related to image style (e.g., photo vs. sketch images). Such style information is captured by the bottom layers of a CNN where our proposed style-mixing takes place. Mixing styles of training instances results in novel domains being synthesized implicitly, which increase the domain diversity of the source domains, and hence the generalizability of the trained model. MixStyle fits into mini-batch training perfectly and is extremely easy to implement. The effectiveness of MixStyle is demonstrated on a wide range of tasks including category classification, instance retrieval and reinforcement learning. | Published as a conference paper at ICLR 2021 DOMAIN GENERALIZATION WITH MIXSTYLE |
d3329074 | In this paper, we propose an interpretable LSTM recurrent neural network, i.e., multi-variable LSTM for time series with exogenous variables. Currently, widely used attention mechanism in recurrent neural networks mostly focuses on the temporal aspect of data and falls short of characterizing variable importance. To this end, our multi-variable LSTM equipped with tensorized hidden states is developed to learn variable specific representations, which give rise to both temporal and variable level attention. Preliminary experiments demonstrate comparable prediction performance of multi-variable LSTM w.r.t. encoder-decoder based baselines. More interestingly, variable importance in real datasets characterized by the variable attention is highly in line with that determined by statistical Granger causality test, which exhibits the prospect of multi-variable LSTM as a simple and uniform end-to-end framework for both forecasting and knowledge discovery. * Equal contribution. | Workshop track -ICLR 2018 AN INTERPRETABLE LSTM NEURAL NETWORK FOR AUTOREGRESSIVE EXOGENOUS MODEL |
d252780848 | Inverse graphics aims to recover 3D models from 2D observations. Utilizing differentiable rendering, recent 3D-aware generative models have shown impressive results of rigid object generation using 2D images. However, it remains challenging to generate articulated objects, like human bodies, due to their complexity and diversity in poses and appearances. In this work, we propose, EVA3D, an unconditional 3D human generative model learned from 2D image collections only. EVA3D can sample 3D humans with detailed geometry and render high-quality images (up to 512 × 256) without bells and whistles (e.g. super resolution). At the core of EVA3D is a compositional human NeRF representation, which divides the human body into local parts. Each part is represented by an individual volume. This compositional representation enables 1) inherent human priors, 2) adaptive allocation of network parameters, 3) efficient training and rendering. Moreover, to accommodate for the characteristics of sparse 2D human image collections (e.g. imbalanced pose distribution), we propose a pose-guided sampling strategy for better GAN learning. Extensive experiments validate that EVA3D achieves state-of-the-art 3D human generation performance regarding both geometry and texture quality. Notably, EVA3D demonstrates great potential and scalability to "inverse-graphics" diverse human bodies with a clean framework. Project page: https://hongfz16.github.io/projects/EVA3D.html. | EVA3D: COMPOSITIONAL 3D HUMAN GENERATION FROM 2D IMAGE COLLECTIONS Figure 1: EVA3D generates high-quality and diverse 3D humans with photo-realistic RGB render- ings and detailed geometry. Only 2D image collections are used for training |
d258236460 | Deep reinforcement learning algorithms that learn policies by trial-and-error must learn from limited amounts of data collected by actively interacting with the environment. While many prior works have shown that proper regularization techniques are crucial for enabling data-efficient RL, a general understanding of the bottlenecks in data-efficient RL has remained unclear. Consequently, it has been difficult to devise a universal technique that works well across all domains. In this paper, we attempt to understand the primary bottleneck in sample-efficient deep RL by examining several potential hypotheses such as non-stationarity, excessive action distribution shift, and overfitting. We perform thorough empirical analysis on state-based DeepMind control suite (DMC) tasks in a controlled and systematic way to show that high temporal-difference (TD) error on the validation set of transitions is the main culprit that severely affects the performance of deep RL algorithms, and prior methods that lead to good performance do in fact, control the validation TD error to be low. This observation gives us a robust principle for making deep RL efficient: we can hill-climb on the validation TD error by utilizing any form of regularization techniques from supervised learning. We show that a simple online model selection method that targets the validation TD error is effective across state-based DMC and Gym tasks. | Published as a conference paper at ICLR 2023 EFFICIENT DEEP REINFORCEMENT LEARNING REQUIRES REGULATING OVERFITTING |
d248496160 | State-of-the-art neural network verifiers are fundamentally based on one of two paradigms: either encoding the whole verification problem via tight multi-neuron convex relaxations or applying a Branch-and-Bound (BaB) procedure leveraging imprecise but fast bounding methods on a large number of easier subproblems. The former can capture complex multi-neuron dependencies but sacrifices completeness due to the inherent limitations of convex relaxations. The latter enables complete verification but becomes increasingly ineffective on larger and more challenging networks. In this work, we present a novel complete verifier which combines the strengths of both paradigms: it leverages multi-neuron relaxations to drastically reduce the number of subproblems generated during the BaB process and an efficient GPU-based dual optimizer to solve the remaining ones. An extensive evaluation demonstrates that our verifier achieves a new stateof-the-art on both established benchmarks as well as networks with significantly higher accuracy than previously considered. The latter result (up to 28% certification gains) indicates meaningful progress towards creating verifiers that can handle practically relevant networks. | Published as a conference paper at ICLR 2022 COMPLETE VERIFICATION VIA MULTI-NEURON RELAXATION GUIDED BRANCH-AND-BOUND |
d3273601 | Disentangling factors of variation has always been a challenging problem in representation learning. Existing algorithms suffer from many limitations, such as unpredictable disentangling factors, bad quality of generated images from encodings, lack of identity information, etc. In this paper, we propose a supervised algorithm called DNA-GAN trying to disentangle different attributes of images. The latent representations of images are DNA-like, in which each individual piece represents an independent factor of variation. By annihilating the recessive piece and swapping a certain piece of two latent representations, we obtain another two different representations which could be decoded into images. In order to obtain realistic images and also disentangled representations, we introduce the discriminator for adversarial training. Experiments on Multi-PIE and CelebA datasets demonstrate the effectiveness of our method and the advantage of overcoming limitations existing in other methods. | DNA-GAN: LEARNING DISENTANGLED REPRESEN- TATIONS FROM MULTI-ATTRIBUTE IMAGES |
d2926851 | We describe a new class of learning models called memory networks. Memory networks reason with inference components combined with a long-term memory component; they learn how to use these jointly. The long-term memory can be read and written to, with the goal of using it for prediction. We investigate these models in the context of question answering (QA) where the long-term memory effectively acts as a (dynamic) knowledge base, and the output is a textual response. We evaluate them on a large-scale QA task, and a smaller, but more complex, toy task generated from a simulated world. In the latter, we show the reasoning power of such models by chaining multiple supporting sentences to answer questions that require understanding the intension of verbs. | Memory Networks |
d221761146 | Pre-trained models for programming language have achieved dramatic empirical improvements on a variety of code-related tasks such as code search, code completion, code summarization, etc. However, existing pre-trained models regard a code snippet as a sequence of tokens, while ignoring the inherent structure of code, which provides crucial code semantics and would enhance the code understanding process. We present GraphCodeBERT, a pre-trained model for programming language that considers the inherent structure of code. Instead of taking syntactic-level structure of code like abstract syntax tree (AST), we use data flow in the pre-training stage, which is a semantic-level structure of code that encodes the relation of "wherethe-value-comes-from" between variables. Such a semantic-level structure is less complex and does not bring an unnecessarily deep hierarchy of AST, the property of which makes the model more efficient. We develop GraphCodeBERT based on Transformer. In addition to using the task of masked language modeling, we introduce two structure-aware pre-training tasks. One is to predict code structure edges, and the other is to align representations between source code and code structure. We implement the model in an efficient way with a graph-guided masked attention function to incorporate the code structure. We evaluate our model on four tasks, including code search, clone detection, code translation, and code refinement. Results show that code structure and newly introduced pre-training tasks can improve GraphCodeBERT and achieves state-of-the-art performance on the four downstream tasks. We further show that the model prefers structure-level attentions over token-level attentions in the task of code search. 1 Published as a conference paper at ICLR 2021In this work, we present GraphCodeBERT, a pre-trained model for programming language that considers the inherent structure of code. Instead of taking syntactic-level structure of code like abstract syntax tree (AST), we leverage semantic-level information of code, i.e. data flow, for pretraining. Data flow is a graph, in which nodes represent variables and edges represent the relation of "where-the-value-comes-from" between variables. Compared with AST, data flow is less complex and does not bring an unnecessarily deep hierarchy, the property of which makes the model more efficient. In order to learn code representation from source code and code structure, we introduce two new structure-aware pre-training tasks. One is data flow edges prediction for learning representation from code structure, and the other is variable-alignment across source code and data flow for aligning representation between source code and code structure. GraphCodeBERT is based on Transformer neural architecture(Vaswani et al., 2017)and we extend it by introducing a graph-guided masked attention function to incorporate the code structure.We pre-train GraphCodeBERT on the CodeSearchNet dataset(Husain et al., 2019), which includes 2.3M functions of six programming languages paired with natural language documents. We evaluate the model on four downstream tasks: natural language code search, clone detection, code translation, and code refinement. Experiments show that our model achieves state-of-the-art performance on the four tasks. Further analysis shows that code structure and newly introduced pre-training tasks can improve GraphCodeBERT and the model has consistent preference for attending data flow.In summary, the contributions of this paper are: (1) GraphCodeBERT is the first pre-trained model that leverages semantic structure of code to learn code representation. (2) We introduce two new structure-aware pre-training tasks for learning representation from source code and data flow.(3)GraphCodeBERT provides significant improvement on four downstream tasks, i.e. code search, clone detection, code translation, and code refinement. | Published as a conference paper at ICLR 2021 GRAPHCODEBERT: PRE-TRAINING CODE REPRESEN- TATIONS WITH DATA FLOW |
d17682909 | Convolutional Neural Networks are extremely efficient architectures in image and audio recognition tasks, thanks to their ability to exploit the local translational invariance of signal classes over their domain. In this paper we consider possible generalizations of CNNs to signals defined on more general domains without the action of a translation group. In particular, we propose two constructions, one based upon a hierarchical clustering of the domain, and another based on the spectrum of the graph Laplacian. We show through experiments that for lowdimensional graphs it is possible to learn convolutional layers with a number of parameters independent of the input size, resulting in efficient deep architectures. | Spectral Networks and Deep Locally Connected Networks on Graphs |
d8895303 | We consider whether deep convolutional networks (CNNs) can represent decision functions with similar accuracy as recurrent networks such as LSTMs. First, we show that a deep CNN with an architecture inspired by the models recently introduced in image recognition can yield better accuracy than previous convolutional and LSTM networks on the standard 309h Switchboard automatic speech recognition task. Then we show that even more accurate CNNs can be trained under the guidance of LSTMs using a variant of model compression, which we call model blending because the teacher and student models are similar in complexity but different in inductive bias. Blending further improves the accuracy of our CNN, yielding a computationally efficient model of accuracy higher than any of the other individual models. Examining the effect of "dark knowledge" in this model compression task, we find that less than 1% of the highest probability labels are needed for accurate model compression. | Workshop track -ICLR 2016 BLENDING LSTMS INTO CNNS |
d249889348 | When deploying Reinforcement Learning (RL) agents into a physical system, we must ensure that these agents are well aware of the underlying constraints. In many real-world problems, however, the constraints are often hard to specify mathematically and unknown to the RL agents. To tackle these issues, Inverse Constrained Reinforcement Learning (ICRL) empirically estimates constraints from expert demonstrations. As an emerging research topic, ICRL does not have common benchmarks, and previous works tested algorithms under hand-crafted environments with manually-generated expert demonstrations. In this paper, we construct an ICRL benchmark in the context of RL application domains, including robot control, and autonomous driving. For each environment, we design relevant constraints and train expert agents to generate demonstration data. Besides, unlike existing baselines that learn a "point estimate" constraint, we propose a variational ICRL method to model a posterior distribution of candidate constraints. We conduct extensive experiments on these algorithms under our benchmark and show how they can facilitate studying important research challenges for ICRL. The benchmark, including the instructions for reproducing ICRL algorithms, is available at https://github.com/Guiliang/ICRL-benchmarks-public. | Published as a conference paper at ICLR 2023 BENCHMARKING CONSTRAINT INFERENCE IN IN- VERSE REINFORCEMENT LEARNING |
d247447758 | The discovery of sparse subnetworks that are able to perform as well as full models has found broad applied and theoretical interest. While many pruning methods have been developed to this end, the naïve approach of removing parameters based on their magnitude has been found to be as robust as more complex, state-of-theart algorithms. The lack of theory behind magnitude pruning's success, especially pre-convergence, and its relation to other pruning methods, such as gradient based pruning, are outstanding open questions in the field that are in need of being addressed. We make use of recent advances in dynamical systems theory, namely Koopman operator theory, to define a new class of theoretically motivated pruning algorithms. We show that these algorithms can be equivalent to magnitude and gradient based pruning, unifying these seemingly disparate methods, and find that they can be used to shed light on magnitude pruning's performance during the early part of training. | Published as a conference paper at ICLR 2022 AN OPERATOR THEORETIC VIEW ON PRUNING DEEP NEURAL NETWORKS |
d256105701 | The large number of ReLU non-linearity operations in existing deep neural networks makes them ill-suited for latency-efficient private inference (PI). Existing techniques to reduce ReLU operations often involve manual effort and sacrifice significant accuracy. In this paper, we first present a novel measure of non-linearity layers' ReLU sensitivity, enabling mitigation of the time-consuming manual efforts in identifying the same. Based on this sensitivity, we then present SENet, a three-stage training method that for a given ReLU budget, automatically assigns per-layer ReLU counts, decides the ReLU locations for each layer's activation map, and trains a model with significantly fewer ReLUs to potentially yield latency and communication efficient PI. Experimental evaluations with multiple models on various datasets show SENet's superior performance both in terms of reduced ReLUs and improved classification accuracy compared to existing alternatives. In particular, SENet can yield models that require up to ∼2× fewer Re-LUs while yielding similar accuracy. For a similar ReLU budget SENet can yield models with ∼2.32% improved classification accuracy, evaluated on CIFAR-100. | Published as a conference paper at ICLR 2023 LEARNING TO LINEARIZE DEEP NEURAL NETWORKS FOR SECURE AND EFFICIENT PRIVATE INFERENCE |
d256900618 | Diffusion probabilistic models (DPMs) have become a popular approach to conditional generation, due to their promising results and support for cross-modal synthesis. A key desideratum in conditional synthesis is to achieve high correspondence between the conditioning input and generated output. Most existing methods learn such relationships implicitly, by incorporating the prior into the variational lower bound. In this work, we take a different route-we explicitly enhance input-output connections by maximizing their mutual information. To this end, we introduce a Conditional Discrete Contrastive Diffusion (CDCD) loss and design two contrastive diffusion mechanisms to effectively incorporate it into the denoising process, combining the diffusion training and contrastive learning for the first time by connecting it with the conventional variational objectives. We demonstrate the efficacy of our approach in evaluations with diverse multimodal conditional synthesis tasks: dance-to-music generation, text-to-image synthesis, as well as class-conditioned image synthesis. On each, we enhance the inputoutput correspondence and achieve higher or competitive general synthesis quality. Furthermore, the proposed approach improves the convergence of diffusion models, reducing the number of required diffusion steps by more than 35% on two benchmarks, significantly increasing the inference speed. | DISCRETE CONTRASTIVE DIFFUSION FOR CROSS- MODAL MUSIC AND IMAGE GENERATION |
d203902511 | Modern deep learning methods provide effective means to learn good representations. However, is a good representation itself sufficient for sample efficient reinforcement learning? This question has largely been studied only with respect to (worst-case) approximation error, in the more classical approximate dynamic programming literature. With regards to the statistical viewpoint, this question is largely unexplored, and the extant body of literature mainly focuses on conditions which permit sample efficient reinforcement learning with little understanding of what are necessary conditions for efficient reinforcement learning. This work shows that, from the statistical viewpoint, the situation is far subtler than suggested by the more traditional approximation viewpoint, where the requirements on the representation that suffice for sample efficient RL are even more stringent. Our main results provide sharp thresholds for reinforcement learning methods, showing that there are hard limitations on what constitutes good function approximation (in terms of the dimensionality of the representation), where we focus on natural representational conditions relevant to value-based, model-based, and policy-based learning. These lower bounds highlight that having a good (valuebased, model-based, or policy-based) representation in and of itself is insufficient for efficient reinforcement learning, unless the quality of this approximation passes certain hard thresholds. Furthermore, our lower bounds also imply exponential separations on the sample complexity between 1) value-based learning with perfect representation and value-based learning with a good-but-not-perfect representation, 2) value-based learning and policy-based learning, 3) policy-based learning and supervised learning and 4) reinforcement learning and imitation learning. bounds for design matrices with applications to combinatorial geometry and locally correctable codes. In Proceedings of the forty-third annual ACM symposium on Theory of computing, pp. 519-528. ACM, 2011.Dimitri P Bertsekas and John N Tsitsiklis. Neuro-dynamic programming, volume 5. Athena Scientific Belmont, MA, 1996.Jinglin Chen and Nan Jiang. Information-theoretic considerations in batch reinforcement learning. arXiv preprint arXiv:1905.00360, 2019.Lijie Chen and Ruosong Wang. Classical algorithms from quantum and arthur-merlin communication protocols.ford. Provably efficient RL with rich observations via latent state decoding. arXiv preprint arXiv:1901.09018, 2019a.Simon S Du, Yuping Luo, Ruosong Wang, and Hanrui Zhang. Provably efficient Q-learning with function approximation via distribution shift error checking oracle. arXiv preprint arXiv:1906.06321, 2019b.Amir-massoud Farahmand. Regularization in reinforcement learning. 2011.Matthieu Geist, Bruno Scherrer, and Olivier Pietquin. A theory of regularized markov decision processes. arXiv preprint arXiv:1901.11275, 2019. Schapire. Contextual decision processes with low bellman rank are PAC-learnable. In | Published as a conference paper at ICLR 2020 IS A GOOD REPRESENTATION SUFFICIENT FOR SAM- PLE EFFICIENT REINFORCEMENT LEARNING? |
d232269768 | In recent years, Generative Adversarial Networks have become ubiquitous in both research and public perception, but how GANs convert an unstructured latent code to a high quality output is still an open question. In this work, we investigate regression into the latent space as a probe to understand the compositional properties of GANs. We find that combining the regressor and a pretrained generator provides a strong image prior, allowing us to create composite images from a collage of random image parts at inference time while maintaining global consistency. To compare compositional properties across different generators, we measure the trade-offs between reconstruction of the unrealistic input and image quality of the regenerated samples. We find that the regression approach enables more localized editing of individual image parts compared to direct editing in the latent space, and we conduct experiments to quantify this independence effect. Our method is agnostic to the semantics of edits, and does not require labels or predefined concepts during training. Beyond image composition, our method extends to a number of related applications, such as image inpainting or example-based image editing, which we demonstrate on several GANs and datasets, and because it uses only a single forward pass, it can operate in real-time. Code is available on our project page: https://chail.github.io/latent-composition/. | Published as a conference paper at ICLR 2021 USING LATENT SPACE REGRESSION TO ANALYZE AND LEVERAGE COMPOSITIONALITY IN GANS |
d257482853 | Recurrent neural networks (RNNs) are well suited for solving sequence tasks in resource-constrained systems due to their expressivity and low computational requirements. However, there is still a need to bridge the gap between what RNNs are capable of in terms of efficiency and performance and real-world application requirements. The memory and computational requirements arising from propagating the activations of all the neurons at every time step to every connected neuron, together with the sequential dependence of activations, contribute to the inefficiency of training and using RNNs. We propose a solution inspired by biological neuron dynamics that makes the communication between RNN units sparse and discrete. This makes the backward pass with backpropagation through time (BPTT) computationally sparse and efficient as well. We base our model on the gated recurrent unit (GRU), extending it with units that emit discrete events for communication triggered by a threshold so that no information is communicated to other units in the absence of events. We show theoretically that the communication between units, and hence the computation required for both the forward and backward passes, scales with the number of events in the network. Our model achieves efficiency without compromising task performance, demonstrating competitive performance compared to state-of-the-art recurrent network models in real-world tasks, including language modeling. The dynamic activity sparsity mechanism also makes our model well suited for novel energy-efficient neuromorphic hardware. Code is available at https://github.com/KhaleelKhan/EvNN/. computation on the previous time step's output prevents easy parallelisation of the model computation. Moreover, propagating the activations of all the units in each time step is computationally inefficient and leads to high memory requirements when training with backpropagation through time (BPTT).While allowing extraordinary task performance, the biological brain's recurrent architecture is extremely energy efficient (Mead, 2020). One of the brain's strategies to reach these high levels of efficiency is activity sparsity. In the brain, (asynchronous) event-based and activity-sparse communication results from the properties of the specific physical and biological substrate on which * Work done while at Ruhr University Bochum Published as a conference paper at ICLR 2023 the brain is built. Biologically realistic spiking neural networks and neuromorphic hardware aim to use these principles to build energy-efficient software and hardware models (Roy et al., 2019; Schuman et al., 2017). However, despite progress in recent years, their task performance has been relatively limited for real-world tasks compared to recurrent architectures based on LSTM and GRU.In this work, we propose an activity sparsity mechanism inspired by biological neuron models, to reduce the computation required by RNNs at each time step. Our method adds a mechanism to the recurrent units to emit discrete events for communication triggered by a threshold so that no information is communicated to other units in the absence of events. With event-based communication, units in the model can decide when to send updates to other units, which then trigger the update of receiving units. When events are sent sparingly, this leads to activity-sparsity where most units do not send updates to other units most of the time, leading to substantial computational savings during training and inference. We formulate the gradient updates of the network to be sparse using a novel method, extending the benefit of the computational savings to training time. We theoretically show, in the continuous time limit, that the time complexity of calculating weight updates is proportional to the number of events in the network. We demonstrate these properties using Gated Recurrent Unit (GRU)(Cho et al., 2014)as a case study, and call our model Event-based Gated Recurrent Unit (EGRU). We note, however, that our dynamic activity-sparsity mechanism can be applied to any RNN architecture.In summary, the main contributions of this paper are the following:1. We introduce a variant of the GRU with an event-generating mechanism, called the EGRU.2. We theoretically show that, in the continuous time limit, both the forward pass computation and the computation of parameter updates in the EGRU scales with the number of events (active units).3. We demonstrate that the EGRU exhibits task-performance competitive with state-of-the-art recurrent network architectures on real-world machine learning benchmarks.4. We empirically show that EGRU exhibits high levels of activity-sparsity during both inference (forward pass) and learning (backward pass).We note here that methods for training with parameter sparsity or improving handling of long-term dependencies are both orthogonal to, and can be combined with our approach (which we plan to do in future work). Our focus, in this paper, is exclusively on using activity-sparsity to increase the efficiency of RNNs, specifically the GRU. We expect our method to be more efficient but not better at handling long-range dependencies compared to the GRU.The sparsity of the backward-pass overcomes one of the major roadblocks in using large recurrent models, which is having enough computational resources to train them. We demonstrate the task performance and activity sparsity of the model implemented in PyTorch, but this formulation will also allow the model to run efficiently on CPU-based nodes when implemented using appropriate software paradigms. Moreover, an implementation on novel neuromorphic hardware like Davies et al. (2018); Höppner et al. (2017), that is geared towards event-based computation, can make the model orders of magnitude more energy efficient (Ostrau et al., 2022). | Published as a conference paper at ICLR 2023 EFFICIENT RECURRENT ARCHITECTURES THROUGH ACTIVITY SPARSITY AND SPARSE BACK-PROPAGATION THROUGH TIME |
d257102428 | Physical simulations that accurately model reality are crucial for many engineering disciplines such as mechanical engineering and robotic motion planning. In recent years, learned Graph Network Simulators produced accurate mesh-based simulations while requiring only a fraction of the computational cost of traditional simulators. Yet, the resulting predictors are confined to learning from data generated by existing mesh-based simulators and thus cannot include real world sensory information such as point cloud data. As these predictors have to simulate complex physical systems from only an initial state, they exhibit a high error accumulation for long-term predictions. In this work, we integrate sensory information to ground Graph Network Simulators on real world observations. In particular, we predict the mesh state of deformable objects by utilizing point cloud data. The resulting model allows for accurate predictions over longer time horizons, even under uncertainties in the simulation, such as unknown material properties. Since point clouds are usually not available for every time step, especially in online settings, we employ an imputation-based model. The model can make use of such additional information only when provided, and resorts to a standard Graph Network Simulator, otherwise. We experimentally validate our approach on a suite of prediction tasks for mesh-based interactions between soft and rigid bodies. Our method results in utilization of additional point cloud information to accurately predict stable simulations where existing Graph Network Simulators | Published as a conference paper at ICLR 2023 GROUNDING GRAPH NETWORK SIMULATORS USING PHYSICAL SENSOR OBSERVATIONS |
d257364759 | This work studies the threats of adversarial attack on multivariate probabilistic forecasting models and viable defense mechanisms. Our studies discover a new attack pattern that negatively impact the forecasting of a target time series via making strategic, sparse (imperceptible) modifications to the past observations of a small number of other time series. To mitigate the impact of such attack, we have developed two defense strategies. First, we extend a previously developed randomized smoothing technique in classification to multivariate forecasting scenarios. Second, we develop an adversarial training algorithm that learns to create adversarial examples and at the same time optimizes the forecasting model to improve its robustness against such adversarial simulation. Extensive experiments on real-world datasets confirm that our attack schemes are powerful and our defense algorithms are more effective compared with baseline defense mechanisms.Published as a conference paper at ICLR 2023 for a selected subset of stock indices (not including the target stock) which is arguably harder to detect. Interestingly, despite being seemingly plausible given the vast literature on adversarial attack for classification models, formulating such imperceptible attack under a multivariate forecasting setup is not straightforward. This is due to several differences between forecasting and classification, particularly in terms of unique characteristic of time series, e.g., multi-step predictions, correlation over multiple time series, and probabilistic predictions. | Published as a conference paper at ICLR 2023 ROBUST MULTIVARIATE TIME-SERIES FORECASTING: ADVERSARIAL ATTACKS AND DEFENSE MECHANISMS |
d231807280 | The recent paper byByrd & Lipton (2019), based on empirical observations, raises a major concern on the impact of importance weighting for the over-parameterized deep learning models. They observe that as long as the model can separate the training data, the impact of importance weighting diminishes as the training proceeds. Nevertheless, there lacks a rigorous characterization of this phenomenon. In this paper, we provide formal characterizations and theoretical justifications on the role of importance weighting with respect to the implicit bias of gradient descent and margin-based learning theory. We reveal both the optimization dynamics and generalization performance under deep learning models. Our work not only explains the various novel phenomenons observed for importance weighting in deep learning, but also extends to the studies where the weights are being optimized as part of the model, which applies to a number of topics under active research. | UNDERSTANDING THE ROLE OF IMPORTANCE WEIGHT- ING FOR DEEP LEARNING |
d208637407 | Modern deep neural networks can achieve high accuracy when the training distribution and test distribution are identically distributed, but this assumption is frequently violated in practice. When the train and test distributions are mismatched, accuracy can plummet. Currently there are few techniques that improve robustness to unforeseen data shifts encountered during deployment. In this work, we propose a technique to improve the robustness and uncertainty estimates of image classifiers. We propose AUGMIX, a data processing technique that is simple to implement, adds limited computational overhead, and helps models withstand unforeseen corruptions. AUGMIX significantly improves robustness and uncertainty measures on challenging image classification benchmarks, closing the gap between previous methods and the best possible performance in some cases by more than half. | Published as a conference paper at ICLR 2020 AUGMIX: A SIMPLE DATA PROCESSING METHOD TO IMPROVE ROBUSTNESS AND UNCERTAINTY |
d232185174 | Several works have shown that the regularization mechanisms underlying deep neural networks' generalization performances are still poorly understood(Neyshabur et al., 2015;Zhang et al., 2017). In this paper, we hypothesize that deep neural networks are regularized through their ability to extract meaningful clusters among the samples of a class. This constitutes an implicit form of regularization, as no explicit training mechanisms or supervision target such behaviour. To support our hypothesis, we design four different measures of intraclass clustering, based on the neuron-and layer-level representations of the training data. We then show that these measures constitute accurate predictors of generalization performance across variations of a large set of hyperparameters (learning rate, batch size, optimizer, weight decay, dropout rate, data augmentation, network depth and width). | Published as a conference paper at ICLR 2021 INTRACLASS CLUSTERING: AN IMPLICIT LEARNING ABILITY THAT REGULARIZES DNNS |
d53116133 | Words are not created equal. In fact, they form an aristocratic graph with a latent hierarchical structure that the next generation of unsupervised learned word embeddings should reveal. In this paper, justified by the notion of delta-hyperbolicity or tree-likeliness of a space, we propose to embed words in a Cartesian product of hyperbolic spaces which we theoretically connect to the Gaussian word embeddings and their Fisher geometry. This connection allows us to introduce a novel principled hypernymy score for word embeddings. Moreover, we adapt the well-known Glove algorithm to learn unsupervised word embeddings in this type of Riemannian manifolds. We further explain how to solve the analogy task using the Riemannian parallel transport that generalizes vector arithmetics to this new type of geometry. Empirically, based on extensive experiments, we prove that our embeddings, trained unsupervised, are the first to simultaneously outperform strong and popular baselines on the tasks of similarity, analogy and hypernymy detection. In particular, for word hypernymy, we obtain new state-of-the-art on fully unsupervised WBLESS classification accuracy. * All authors contributed equally. | POINCARÉ GLOVE: HYPERBOLIC WORD EMBEDDINGS |
d253238033 | Fully-parametric language models generally require a huge number of model parameters to store the necessary knowledge for solving multiple natural language tasks in zero/few-shot settings. In addition, it is hard to adapt to the evolving world knowledge without the costly model re-training. In this paper, we develop a novel semi-parametric language model architecture, Knowledge-in-Context (KiC), which empowers a parametric text-to-text language model with a knowledgerich external memory. Specifically, the external memory contains six different types of knowledge: entity, dictionary, commonsense, event, script, and causality knowledge. For each input instance, the KiC model adaptively selects a knowledge type and retrieves the most helpful pieces of knowledge. The input instance along with its knowledge augmentation is fed into a text-to-text model (e.g., T5) to generate the output answer, where both the input and the output are in natural language forms after prompting. Interestingly, we find that KiC can be identified as a special mixture-of-experts (MoE) model, where the knowledge selector plays the role of a router that is used to determine the sequence-to-expert assignment in MoE. This key observation inspires us to develop a novel algorithm for training KiC with an instance-adaptive knowledge selector. As a knowledge-rich semiparametric language model, KiC only needs a much smaller parametric part to achieve superior zero-shot performance on unseen tasks. By evaluating on 40+ different tasks, we show that KiC Large with 770M parameters easily outperforms large language models that are 4-39x larger. In addition, KiC also exhibits emergent abilities at a much smaller model scale compared to the fully-parametric models. * | KNOWLEDGE-IN-CONTEXT: TOWARDS KNOWLEDGE- ABLE SEMI-PARAMETRIC LANGUAGE MODELS |
d256697616 | Random-feature-based attention (RFA) is an efficient approximation of softmax attention with linear runtime and space complexity. However, the approximation gap between RFA and conventional softmax attention is not well studied. Built upon previous progress of RFA, we characterize this gap through the lens of control variates and show that RFA can be decomposed into a sum of multiple control variate estimators for each element in the sequence. This new framework reveals that exact softmax attention can be recovered from RFA by manipulating each control variate. Besides, it allows us to develop a more flexible form of control variates, resulting in a novel attention mechanism that significantly reduces the approximation gap while maintaining linear complexity. Extensive experiments demonstrate that our model outperforms state-of-the-art efficient attention mechanisms on both vision and language tasks. 1 * The majority of this work was done while these authors were at Bytedance. 1 Our code and models are available at this link. | Published as a conference paper at ICLR 2023 EFFICIENT ATTENTION VIA CONTROL VARIATES |
d237605600 | This article considers the popular MCMC method of unadjusted Langevin Monte Carlo (LMC) and provides a non-asymptotic analysis of its sampling error in 2-Wasserstein distance. The proof is based on a refinement of mean-square analysis in Li et al.(2019), and this refined framework automates the analysis of a large class of sampling algorithms based on discretizations of contractive SDEs. Using this framework, we establish an O √ d / mixing time bound for LMC, without warm start, under the common log-smooth and log-strongly-convex conditions, plus a growth condition on the 3rd-order derivative of the potential of target measures. This bound improves the best previously known O d / result and is optimal (in terms of order) in both dimension d and accuracy tolerance for target measures satisfying the aforementioned assumptions. Our theoretical analysis is further validated by numerical experiments. | Published as a conference paper at ICLR 2022 SQRT(D) DIMENSION DEPENDENCE OF LANGEVIN MONTE CARLO |
d232335748 | Concentration of measure has been argued to be the fundamental cause of adversarial vulnerability. Mahloujifar et al. (2019b) presented an empirical way to measure the concentration of a data distribution using samples, and employed it to find lower bounds on intrinsic robustness for several benchmark datasets. However, it remains unclear whether these lower bounds are tight enough to provide a useful approximation for the intrinsic robustness of a dataset. To gain a deeper understanding of the concentration of measure phenomenon, we first extend the Gaussian Isoperimetric Inequality to non-spherical Gaussian measures and arbitrary p -norms (p ≥ 2). We leverage these theoretical insights to design a method that uses half-spaces to estimate the concentration of any empirical dataset under p -norm distance metrics. Our proposed algorithm is more efficient than Mahloujifar et al. (2019b)'s, and our experiments on synthetic datasets and image benchmarks demonstrate that it is able to find much tighter intrinsic robustness bounds. These tighter estimates provide further evidence that rules out intrinsic dataset concentration as a possible explanation for the adversarial vulnerability of state-of-the-art classifiers. | IMPROVED ESTIMATION OF CONCENTRATION UNDER p -NORM DISTANCE METRICS USING HALF SPACES |
d210713887 | This paper proposes the use of spectral element methods(Canuto et al., 1988)for fast and accurate training of Neural Ordinary Differential Equations (ODE-Nets;Chen et al., 2018). This is achieved by expressing their dynamics as truncated series of Legendre polynomials. The series coefficients, as well as the network weights, are computed by minimizing the weighted sum of the loss function and the violation of the ODE-Net dynamics. The problem is solved by coordinate descent that alternately minimizes, with respect to the coefficients and the weights, two unconstrained sub-problems using standard backpropagation and gradient methods. The resulting optimization scheme is fully time-parallel and results in a low memory footprint. Experimental comparison to standard methods, such as backpropagation through explicit solvers and the adjoint technique (Chen et al., 2018), on training surrogate models of small and medium-scale dynamical systems shows that it is at least one order of magnitude faster at reaching a comparable value of the loss function. The corresponding testing MSE is one order of magnitude smaller as well, suggesting generalization capabilities increase. | Accelerating Neural ODEs with Spectral Elements |
d257353697 | Neural ordinary differential equations (Neural ODEs) are an effective framework for learning dynamical systems from irregularly sampled time series data. These models provide a continuous-time latent representation of the underlying dynamical system where new observations at arbitrary time points can be used to update the latent representation of the dynamical system. Existing parameterizations for the dynamics functions of Neural ODEs limit the ability of the model to retain global information about the time series; specifically, a piece-wise integration of the latent process between observations can result in a loss of memory on the dynamic patterns of previously observed data points. We propose PolyODE, a Neural ODE that models the latent continuous-time process as a projection onto a basis of orthogonal polynomials. This formulation enforces long-range memory and preserves a global representation of the underlying dynamical system. Our construction is backed by favourable theoretical guarantees and in a series of experiments, we demonstrate that it outperforms previous works in the reconstruction of past and future data, and in downstream prediction tasks. Our code is available at https://github.com/edebrouwer/polyode.Published as a conference paper at ICLR 2023 ODE formulations are amnesic. We illustrate this effect inFigure 1, where we see that backward integration of a learned neural ODE (that is competent at forecasting) quickly diverges, indicating the state only retains sufficient local information about the future dynamics. | Published as a conference paper at ICLR 2023 ANAMNESIC NEURAL DIFFERENTIAL EQUATIONS WITH ORTHOGONAL POLYNOMIALS PROJECTIONS |
d256390313 | In the area of few-shot anomaly detection (FSAD), efficient visual feature plays an essential role in the memory bank M-based methods.However, these methods do not account for the relationship between the visual feature and its rotated visual feature, drastically limiting the anomaly detection performance.To push the limits, we reveal that rotation-invariant feature property has a significant impact on industrial-based FSAD.Specifically, we utilize graph representation in FSAD and provide a novel visual isometric invariant feature (VIIF) as an anomaly measurement feature.As a result, VIIF can robustly improve the anomaly discriminating ability and can further reduce the size of redundant features stored in M by a large amount.Besides, we provide a novel model GraphCore via VIIFs that can fast implement unsupervised FSAD training and improve the performance of anomaly detection.A comprehensive evaluation is provided for comparing GraphCore and other SOTA anomaly detection models under our proposed few-shot anomaly detection setting, which shows GraphCore can increase average AUC by 5.8%, 4.1%, 3.4%, and 1.6% on MVTec AD and by 25.5%, 22.0%, 16.9%, and 14.1% on MPDD for 1, 2, 4, and 8-shot cases, respectively. | PUSHING THE LIMITS OF FEW-SHOT ANOMALY DE-TECTION IN INDUSTRY VISION: GRAPHCORE |
d249538446 | The generalization of model-based reinforcement learning (MBRL) methods to environments with unseen transition dynamics is an important yet challenging problem.Existing methods try to extract environment-specified information Z from past transition segments to make the dynamics prediction model generalizable to different dynamics.However, because environments are not labelled, the extracted information inevitably contains redundant information unrelated to the dynamics in transition segments and thus fails to maintain a crucial property of Z: Z should be similar in the same environment and dissimilar in different ones.As a result, the learned dynamics prediction function will deviate from the true one, which undermines the generalization ability.To tackle this problem, we introduce an interventional prediction module to estimate the probability of two estimated ẑi , ẑj belonging to the same environment.Furthermore, by utilizing the Z's invariance within a single environment, a relational head is proposed to enforce the similarity between Ẑ from the same environment.As a result, the redundant information will be reduced in Ẑ.We empirically show that Ẑ estimated by our method enjoy less redundant information than previous methods, and such Ẑ can significantly reduce dynamics prediction errors and improve the performance of model-based RL methods on zero-shot new environments with unseen dynamics.The codes of this method are available at https://github.com/CR-Gjx/RIA. | A RELATIONAL INTERVENTION APPROACH FOR UN-SUPERVISED DYNAMICS GENERALIZATION IN MODEL-BASED REINFORCEMENT LEARNING |
d232135338 | The goal of the paper is to design active learning strategies which lead to domain adaptation under an assumption of Lipschitz functions. Building on previous work by Mansour et al. (2009) we adapt the concept of discrepancy distance between source and target distributions to restrict the maximization over the hypothesis class to a localized class of functions which are performing accurate labeling on the source domain. We derive generalization error bounds for such active learning strategies in terms of Rademacher average and localized discrepancy for general loss functions which satisfy a regularity condition. A practical Kmedoids algorithm that can address the case of large data set is inferred from the theoretical bounds. Our numerical experiments show that the proposed algorithm is competitive against other state-of-the-art active learning techniques in the context of domain adaptation, in particular on large data sets of around one hundred thousand images. | DISCREPANCY-BASED ACTIVE LEARNING FOR DO- MAIN ADAPTATION |
d257205872 | Recently, generalization on out-of-distribution (OOD) data with correlation shift has attracted great attentions. The correlation shift is caused by the spurious attributes that correlate to the class label, as the correlation between them may vary in training and test data. For such a problem, we show that given the class label, the models that are conditionally independent of spurious attributes are OOD generalizable. Based on this, a metric Conditional Spurious Variation (CSV) which controls the OOD generalization error, is proposed to measure such conditional independence. To improve the OOD generalization, we regularize the training process with the proposed CSV. Under mild assumptions, our training objective can be formulated as a nonconvex-concave mini-max problem. An algorithm with a provable convergence rate is proposed to solve the problem. Extensive empirical results verify our algorithm's efficacy in improving OOD generalization. . Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. | Published as a conference paper at ICLR 2023 BREAKING CORRELATION SHIFT VIA CONDITIONAL INVARIANT REGULARIZER |
d256826746 | While multi-agent trust region algorithms have achieved great success empirically in solving coordination tasks, most of them, however, suffer from a nonstationarity problem since agents update their policies simultaneously. In contrast, a sequential scheme that updates policies agent-by-agent provides another perspective and shows strong performance. However, sample inefficiency and lack of monotonic improvement guarantees for each agent are still the two significant challenges for the sequential scheme. In this paper, we propose the Agent-byagent Policy Optimization (A2PO) algorithm to improve the sample efficiency and retain the guarantees of monotonic improvement for each agent during training. We justify the tightness of the monotonic improvement bound compared with other trust region algorithms. From the perspective of sequentially updating agents, we further consider the effect of agent updating order and extend the theory of non-stationarity into the sequential update scheme. To evaluate A2PO, we conduct a comprehensive empirical study on four benchmarks: StarCraftII, Multiagent MuJoCo, Multi-agent Particle Environment, and Google Research Football full game scenarios. A2PO consistently outperforms strong baselines. | Published as a conference paper at ICLR 2023 ORDER MATTERS: AGENT-BY-AGENT POLICY OPTI- MIZATION |
d244709097 | Numerous physical systems are described by ordinary or partial differential equations whose solutions are given by holomorphic or meromorphic functions in the complex domain. In many cases, only the magnitude of these functions are observed on various points on the purely imaginary jω-axis since coherent measurement of their phases is often expensive. However, it is desirable to retrieve the lost phases from the magnitudes when possible. To this end, we propose a physics-infused deep neural network based on the Blaschke products for phase retrieval. Inspired by the Helson and Sarason Theorem, we recover coefficients of a rational function of Blaschke products using a Blaschke Product Neural Network (BPNN), based upon the magnitude observations as input. The resulting rational function is then used for phase retrieval. We compare the BPNN to conventional deep neural networks (NNs) on several phase retrieval problems, comprising both synthetic and contemporary real-world problems (e.g., metamaterials for which data collection requires substantial expertise and is time consuming). On each phase retrieval problem, we compare against a population of conventional NNs of varying size and hyperparameter settings. Even without any hyper-parameter search, we find that BPNNs consistently outperform the population of optimized NNs in scarce data scenarios, and do so despite being much smaller models. The results can in turn be applied to calculate the refractive index of metamaterials, which is an important problem in emerging areas of material science. | BLASCHKE PRODUCT NEURAL NETWORK (BPNN): A PHYSICS-INFUSED NEURAL NETWORK FOR PHASE RETRIEVAL OF MEROMORPHIC FUNCTIONS |
d10278413 | Kernel canonical correlation analysis (KCCA) is a nonlinear multi-view representation learning technique with broad applicability in statistics and machine learning. Although there is a closed-form solution for the KCCA objective, it involves solving an N × N eigenvalue system where N is the training set size, making its computational requirements in both memory and time prohibitive for large-scale problems. Various approximation techniques have been developed for KCCA. A commonly used approach is to first transform the original inputs to an M -dimensional random feature space so that inner products in the feature space approximate kernel evaluations, and then apply linear CCA to the transformed inputs. In many applications, however, the dimensionality M of the random feature space may need to be very large in order to obtain a sufficiently good approximation; it then becomes challenging to perform the linear CCA step on the resulting very high-dimensional data matrices. We show how to use a stochastic optimization algorithm, recently proposed for linear CCA and its neuralnetwork extension, to further alleviate the computation requirements of approximate KCCA. This approach allows us to run approximate KCCA on a speech dataset with 1.4 million training samples and a random feature space of dimensionality M = 100000 on a typical workstation. | LARGE-SCALE APPROXIMATE KERNEL CANONICAL CORRELATION ANALYSIS |
d252907554 | Sequence-to-Sequence (seq2seq) tasks transcribe the input sequence to a target sequence. The Connectionist Temporal Classification (CTC) criterion is widely used in multiple seq2seq tasks. Besides predicting the target sequence, a side product of CTC is to predict the alignment, which is the most probable input-long sequence that specifies a hard aligning relationship between the input and target units. As there are multiple potential aligning sequences (called paths) that are equally considered in CTC formulation, the choice of which path will be most probable and become the predicted alignment is always uncertain. In addition, it is usually observed that the alignment predicted by vanilla CTC will drift compared with its reference and rarely provides practical functionalities. Thus, the motivation of this work is to make the CTC alignment prediction controllable and thus equip CTC with extra functionalities. The Bayes risk CTC (BRCTC) criterion is then proposed in this work, in which a customizable Bayes risk function is adopted to enforce the desired characteristics of the predicted alignment. With the risk function, the BRCTC is a general framework to adopt some customizable preference over the paths in order to concentrate the posterior into a particular subset of the paths. In applications, we explore one particular preference which yields models with the down-sampling ability and reduced inference costs. By using BRCTC with another preference for early emissions, we obtain an improved performance-latency trade-off for online models. Experimentally, the proposed BRCTC, along with a trimming approach, enables us to reduce the inference cost of offline models by up to 47% without performance degradation; BRCTC also cuts down the overall latency of online systems to an unseen level 1 . | Published as a conference paper at ICLR 2023 BAYES RISK CTC: CONTROLLABLE CTC ALIGNMENT IN SEQUENCE-TO-SEQUENCE TASKS |
d257404839 | In recent years, contrastive learning achieves impressive results on self-supervised visual representation learning, but there still lacks a rigorous understanding of its learning dynamics. In this paper, we show that if we cast a contrastive objective equivalently into the feature space, then its learning dynamics admits an interpretable form. Specifically, we show that its gradient descent corresponds to a specific message passing scheme on the corresponding augmentation graph. Based on this perspective, we theoretically characterize how contrastive learning gradually learns discriminative features with the alignment update and the uniformity update. Meanwhile, this perspective also establishes an intriguing connection between contrastive learning and Message Passing Graph Neural Networks (MP-GNNs). This connection not only provides a unified understanding of many techniques independently developed in each community, but also enables us to borrow techniques from MP-GNNs to design new contrastive learning variants, such as graph attention, graph rewiring, jumpy knowledge techniques, etc. We believe that our message passing perspective not only provides a new theoretical understanding of contrastive learning dynamics, but also bridges the two seemingly independent areas together, which could inspire more interleaving studies to benefit from each other. The code is available at https://github.com/PKU-ML/ Message-Passing-Contrastive-Learning. | A MESSAGE PASSING PERSPECTIVE ON LEARNING DY- NAMICS OF CONTRASTIVE LEARNING |
d257219304 | It's a meaningful and attractive topic to build a general and inclusive segmentation model that can recognize more categories in various scenarios. A straightforward way is to combine the existing fragmented segmentation datasets and train a multidataset network. However, there are two major issues with multi-dataset segmentation: (i) the inconsistent taxonomy demands manual reconciliation to construct a unified taxonomy; (ii) the inflexible one-hot common taxonomy causes timeconsuming model retraining and defective supervision of unlabeled categories. In this paper, we investigate the multi-dataset segmentation and propose a scalable Language-guided Multi-dataset Segmentation framework, dubbed LMSeg, which supports both semantic and panoptic segmentation. Specifically, we introduce a pre-trained text encoder to map the category names to a text embedding space as a unified taxonomy, instead of using inflexible one-hot label. The model dynamically aligns the segment queries with the category embeddings. Instead of relabeling each dataset with the unified taxonomy, a category-guided decoding module is designed to dynamically guide predictions to each dataset's taxonomy. Furthermore, we adopt a dataset-aware augmentation strategy that assigns each dataset a specific image augmentation pipeline, which can suit the properties of images from different datasets. Extensive experiments demonstrate that our method achieves significant improvements on four semantic and three panoptic segmentation datasets, and the ablation study evaluates the effectiveness of each component. | Published as a conference paper at ICLR 2023 LMSEG: LANGUAGE-GUIDED MULTI-DATASET SEGMENTATION |
d249192149 | Distilling from the feature maps can be fairly effective for dense prediction tasks since both the feature discriminability and localization priors can be well transferred. However, not every pixel contributes equally to the performance, and a good student should learn from what really matters to the teacher. In this paper, we introduce a learnable embedding dubbed receptive token to localize those pixels of interests (PoIs) in the feature map, with a distillation mask generated via pixel-wise attention. Then the distillation will be performed on the mask via pixel-wise reconstruction. In this way, a distillation mask actually indicates a pattern of pixel dependencies within feature maps of teacher. We thus adopt multiple receptive tokens to investigate more sophisticated and informative pixel dependencies to further enhance the distillation. To obtain a group of masks, the receptive tokens are learned via the regular task loss but with teacher fixed, and we also leverage a Dice loss to enrich the diversity of learned masks. Our method dubbed MasKD is simple and practical, and needs no priors of tasks in application. Experiments show that our MasKD can achieve state-of-the-art performance consistently on object detection and semantic segmentation benchmarks. Code is available at https://github.com/hunto/MasKD. * Equal contributions. † Correspondence to: Shan You <youshan@sensetime.com>. 1 Fitnet(Romero et al., 2014)improves Faster RCNN-R50 by only 0.5%, while has no gain on RetinaNet-R50 (seeTable 1). | Published as a conference paper at ICLR 2023 MASKED DISTILLATION WITH RECEPTIVE TOKENS |
d247613032 | 1We study COMP-AMS, a distributed optimization framework based on gradient averaging and adaptive AMSGrad algorithm. Gradient compression with error feedback is applied to reduce the communication cost in the gradient transmission process. Our convergence analysis of COMP-AMS shows that such compressed gradient averaging strategy yields same convergence rate as standard AMSGrad, and also exhibits the linear speedup effect w.r.t. the number of local workers. Compared with recently proposed protocols on distributed adaptive methods, COMP-AMS is simple and convenient. Numerical experiments are conducted to justify the theoretical findings, and demonstrate that the proposed method can achieve same test accuracy as the full-gradient AMSGrad with substantial communication savings. With its simplicity and efficiency, COMP-AMS can serve as a useful distributed training framework for adaptive gradient methods. . Fedsketch: Communication-efficient and private federated learning via sketching. arXiv preprint arXiv:2008.04975, 2020. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In . Prox-pda: The proximal primal-dual algorithm for fast distributed nonconvex optimization and learning over networks. In . A linear speedup analysis of distributed deep learning with sparse and quantized communication. . Error feedback fixes signsgd and other gradient compression schemes. In . Mime: Mimicking centralized stochastic algorithms in federated learning. arXiv preprint arXiv:2008.03606, 2020. Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In . End-to-end training of deep visuomotor policies. . GNSD: a gradient-tracking based nonconvex stochastic algorithm for decentralized optimization. In . Massively distributed SGD: Imagenet/resnet-50 training in a flash. arXiv preprint arXiv:1811.05233, 2018. Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013. Parvin Nazari, Davoud Ataee Tarzanagh, and George Michailidis. Dadam: A consensus-based distributed adaptive gradient method for online optimization. arXiv preprint arXiv:1901.09109, 2019. Angelia Nedic and Asuman E. Ozdaglar. Distributed subgradient methods for multi-agent optimization. | Published as a conference paper at ICLR 2022 ON DISTRIBUTED ADAPTIVE OPTIMIZATION WITH GRADIENT COMPRESSION |
d210861217 | Modelling highly multi-modal data is a challenging problem in machine learning. Most algorithms are based on maximizing the likelihood, which corresponds to the M(oment)-projection of the data distribution to the model distribution. The Mprojection forces the model to average over modes it cannot represent. In contrast, the I(information)-projection ignores such modes in the data and concentrates on the modes the model can represent. Such behavior is appealing whenever we deal with highly multi-modal data where modelling single modes correctly is more important than covering all the modes. Despite this advantage, the I-projection is rarely used in practice due to the lack of algorithms that can efficiently optimize it based on data. In this work, we present a new algorithm called Expected Information Maximization (EIM) for computing the I-projection solely based on samples for general latent variable models, where we focus on Gaussian mixtures models and Gaussian mixtures of experts. Our approach applies a variational upper bound to the I-projection objective which decomposes the original objective into single objectives for each mixture component as well as for the coefficients, allowing an efficient optimization. Similar to GANs, our approach employs discriminators but uses a more stable optimization procedure, using a tight upper bound. We show that our algorithm is much more effective in computing the I-projection than recent GAN approaches and we illustrate the effectiveness of our approach for modelling multi-modal behavior on two pedestrian and traffic prediction datasets. | Published as a conference paper at ICLR 2020 EXPECTED INFORMATION MAXIMIZATION USING THE I-PROJECTION FOR MIXTURE DENSITY ESTIMATION |
d233231739 | Learning to predict the long-term future of video frames is notoriously challenging due to inherent ambiguities in the distant future and dramatic amplifications of prediction error through time. Despite the recent advances in the literature, existing approaches are limited to moderately short-term prediction (less than a few seconds), while extrapolating it to a longer future quickly leads to destruction in structure and content. In this work, we revisit hierarchical models in video prediction. Our method predicts future frames by first estimating a sequence of semantic structures and subsequently translating the structures to pixels by videoto-video translation. Despite the simplicity, we show that modeling structures and their dynamics in the discrete semantic structure space with a stochastic recurrent estimator leads to surprisingly successful long-term prediction. We evaluate our method on three challenging datasets involving car driving and human dancing, and demonstrate that it can generate complicated scene structures and motions over a very long time horizon (i.e., thousands frames), setting a new standard of video prediction with orders of magnitude longer prediction time than existing approaches. Full videos and codes are available at https://1konny.github.io/HVP/. | Published as a conference paper at ICLR 2021 REVISITING HIERARCHICAL APPROACH FOR PERSISTENT LONG-TERM VIDEO PREDICTION |
d252917944 | Machine learning models are increasingly used in high-stakes decision-making systems. In such applications, a major concern is that these models sometimes discriminate against certain demographic groups such as individuals with certain race, gender, or age. Another major concern in these applications is the violation of the privacy of users. While fair learning algorithms have been developed to mitigate discrimination issues, these algorithms can still leak sensitive information, such as individuals' health or financial records. Utilizing the notion of differential privacy (DP), prior works aimed at developing learning algorithms that are both private and fair. However, existing algorithms for DP fair learning are either not guaranteed to converge or require full batch of data in each iteration of the algorithm to converge. In this paper, we provide the first stochastic differentially private algorithm for fair learning that is guaranteed to converge. Here, the term "stochastic" refers to the fact that our proposed algorithm converges even when minibatches of data are used at each iteration (i.e. stochastic optimization). Our framework is flexible enough to permit different fairness notions, including demographic parity and equalized odds. In addition, our algorithm can be applied to non-binary classification tasks with multiple (non-binary) sensitive attributes. As a byproduct of our convergence analysis, we provide the first utility guarantee for a DP algorithm for solving nonconvex-strongly concave min-max problems. Our numerical experiments show that the proposed algorithm consistently offers significant performance gains over the state-of-the-art baselines, and can be applied to larger scale problems with non-binary target/sensitive attributes. | Published as a conference paper at ICLR 2023 STOCHASTIC DIFFERENTIALLY PRIVATE AND FAIR LEARNING |
d14612342 | We address the problem of contour detection via per-pixel classifications of edge point. To facilitate the process, the proposed approach leverages with DenseNet, an efficient implementation of multiscale convolutional neural networks (CNNs), to extract an informative feature vector for each pixel and uses an SVM classifier to accomplish contour detection. In the experiment of contour detection, we look into the effectiveness of combining per-pixel features from different CNN layers and verify their performance on BSDS500. | PIXEL-WISE DEEP LEARNING FOR CONTOUR DETEC- TION |
d3517962 | This work adopts the very successful distributional perspective on reinforcement learning and adapts it to the continuous control setting. We combine this within a distributed framework for off-policy learning in order to develop what we call the Distributed Distributional Deep Deterministic Policy Gradient algorithm, D4PG. We also combine this technique with a number of additional, simple improvements such as the use of N -step returns and prioritized experience replay. Experimentally we examine the contribution of each of these individual components, and show how they interact, as well as their combined contributions. Our results show that across a wide variety of simple control tasks, difficult manipulation tasks, and a set of hard obstacle-based locomotion tasks the D4PG algorithm achieves state of the art performance.arXiv:1804.08617v1 [cs.LG] 23 Apr 2018Published as a conference paper at ICLR 2018 experience, which we implement using the ApeX framework(Horgan et al., 2018). This results in significant savings in terms of wall-clock time for difficult control tasks. We will also introduce a number of small improvements to the DDPG algorithm, and in our experiments will show the individual contributions of each component. Finally, this algorithm, which we call the Distributed Distributional DDPG algorithm (D4PG), obtains state-of-the-art performance across a wide variety of control tasks, including hard manipulation and locomotion tasks. | Published as a conference paper at ICLR 2018 DISTRIBUTED DISTRIBUTIONAL DETERMINISTIC POLICY GRADIENTS |
d245123899 | Humans use natural language to compose common concepts from their environment into plausible, day-to-day scene descriptions. However, such generative commonsense reasoning (GCSR) skills are lacking in state-of-the-art text generation methods. Descriptive sentences about arbitrary concepts generated by neural text generation models (e.g., pre-trained text-to-text Transformers) are often grammatically fluent but may not correspond to human common sense, largely due to their lack of mechanisms to capture concept relations, to identify implicit concepts, and to perform generalizable reasoning about unseen concept compositions. In this paper, we propose an Imagine-and-Verbalize (I&V) method, which learns to imagine a relational scene knowledge graph (SKG) with relations between the input concepts, and leverage the SKG as a constraint when generating a plausible scene description. We collect and harmonize a set of knowledge resources from different domains and modalities, providing a rich auxiliary supervision signal for I&V. The experiments demonstrate the effectiveness of I&V in improving language models on both concept-to-sentence and concept-to-story generation tasks, while enabling the model to learn well from fewer task examples and generate SKGs that make common sense to human annotators 1 . * Equal contributions 1 Code and data are available at https | Published as a conference paper at ICLR 2022 CONTEXTUALIZED SCENE IMAGINATION FOR GENERATIVE COMMONSENSE REASONING |
d3506178 | As neural networks grow deeper and wider, learning networks with hard-threshold activations is becoming increasingly important, both for network quantization, which can drastically reduce time and energy requirements, and for creating large integrated systems of deep networks, which may have non-differentiable components and must avoid vanishing and exploding gradients for effective learning. However, since gradient descent is not applicable to hard-threshold functions, it is not clear how to learn them in a principled way. We address this problem by observing that setting targets for hard-threshold hidden units in order to minimize loss is a discrete optimization problem, and can be solved as such. The discrete optimization goal is to find a set of targets such that each unit, including the output, has a linearly separable problem to solve. Given these targets, the network decomposes into individual perceptrons, which can then be learned with standard convex approaches. Based on this, we develop a recursive mini-batch algorithm for learning deep hardthreshold networks that includes the popular but poorly justified straight-through estimator as a special case. Empirically, we show that our algorithm improves classification accuracy in a number of settings, including for AlexNet and ResNet-18 on ImageNet, when compared to the straight-through estimator. | DEEP LEARNING AS A MIXED CONVEX- COMBINATORIAL OPTIMIZATION PROBLEM |
d51780574 | Deep generative models provide a systematic way to learn nonlinear data distributions through a set of latent variables and a nonlinear "generator" function that maps latent points into the input space. The nonlinearity of the generator implies that the latent space gives a distorted view of the input space. Under mild conditions, we show that this distortion can be characterized by a stochastic Riemannian metric, and we demonstrate that distances and interpolants are significantly improved under this metric. This in turn improves probability distributions, sampling algorithms and clustering in the latent space. Our geometric analysis further reveals that current generators provide poor variance estimates and we propose a new generator architecture with vastly improved variance estimates. Results are demonstrated on convolutional and fully connected variational autoencoders, but the formalism easily generalizes to other deep generative models. | Published as a conference paper at ICLR 2018 LATENT SPACE ODDITY: ON THE CURVATURE OF DEEP GENERATIVE MODELS |
d11212020 | Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and encode a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition. | Published as a conference paper at ICLR 2015 NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE |
d247582435 | Recent work explored the potential of large-scale Transformer-based pre-trained models, especially Pre-trained Language Models (PLMs) in natural language processing. This raises many concerns from various perspectives, e.g., financial costs and carbon emissions. Compressing PLMs like BERT with negligible performance loss for faster inference and cheaper deployment has attracted much attention. In this work, we aim to explore larger compression ratios for PLMs, among which tensor decomposition is a potential but under-investigated one. Two decomposition and reconstruction protocols are further proposed to improve the effectiveness and efficiency during compression. Our compressed BERT 1 with 1/7 parameters in Transformer layers performs on-par with, sometimes slightly better than the original BERT in GLUE benchmark. A tiny version achieves 96.7% performance of BERT-base with 1/48 encoder parameters (i.e., less than 2M parameters excluding the embedding layer) and 2.7× faster on inference. To show that the proposed method is orthogonal to existing compression methods like knowledge distillation, we also explore the benefit of the proposed method on a distilled BERT.Published as a conference paper at ICLR 2022 calculated separately, e.g., attentions among heads act on a similar subspace and are therefore lowrank (Cordonnier et al., 2021) -we relate this phenomenon to the so-called 'decomposability' defined in this paper. Like self-attention layers, decomposability also holds in FFN layers -each FFN layer could be decomposed to many independent sub-FFNs (as explained in Appendix B). One example of inter-matrix redundancy happens across different layers, e.g., attention maps among layers might be similar(Clark et al., 2019;Vig, 2019; Rogers et al., 2020).Exploration of main weight matrices in Transformer layers finds that these weight matrices are possible to be approximated in a low-rank manner -evidencing the possible intra-matrix redundancy and inter-matrix redundancy. We comprehensively analyze and compare different decomposition methods for parameter compression including matrix decomposition (denoted as II), tensor train decomposition (Oseledets, 2011) (denoted as III) and Tucker decomposition(De Lathauwer et al., 2000)(denoted as IV). The fundamental difference between them is as below. II conducts matrix factorization (e.g., SVD) for each weight matrix thanks to intra-matrix redundancy. Regarding intermatrix redundancy, III shares the head and tail matrices while keeping the core matrix individual; IV introduces 'matrix bank' to make parameter scale being nearly constant w.r.t. the number of layers. It is concluded that Tucker decomposition (IV) is more parameter-efficient than others in terms of compression ratios. ALBERT (Lan et al., 2019) and III can be considered as special cases of IV.The practical challenges of matrix/tensor decomposition for compression are twofold. First, the decomposition may result in a discrepancy between the raw weights and approximated weights, and exact decomposition is impossible with large compression ratios. Instead, Knowledge Distillation (KD) is used on the compressed model to simulate the predictions of the raw model in a loss-aware manner. Second, reconstruction may lead to additional computation costs. An efficient reconstruction protocol is implemented by reordering multiplication operations that also preserve the same results.The contributions of this work are (1) we propose a formal framework with standardized terminology to comprehensively discuss matrix/tensor decomposition methods to compress Transformer-based language models; (2) we adopt tensor decomposition for compressing PLMs which is also faster, while existing work(Ma et al., 2019;Liu et al., 2021)did not show the potential for speedup in PLMs;(3) our compressed BERT with 1/7 parameters in Transformer layers performs on-par with the original BERT in GLUE benchmark. Also, a tiny version achieves 96.7% performance of BERT-base with only 1/48 parameters in Transformer layers and 2.7× faster on inference. We directly use the proposed methods on TinyBERT (Jiao et al., 2020) that is purely based on KD, since our work is complementary to existing compression methods like KD. | Published as a conference paper at ICLR 2022 EXPLORING EXTREME PARAMETER COMPRESSION FOR PRE-TRAINED LANGUAGE MODELS |
d257220165 | Denoising diffusion models are a popular class of generative models providing state-of-the-art results in many domains. One adds gradually noise to data using a diffusion to transform the data distribution into a Gaussian distribution. Samples from the generative model are then obtained by simulating an approximation of the time-reversal of this diffusion initialized by Gaussian samples. Practically, the intractable score terms appearing in the time-reversed process are approximated using score matching techniques. We explore here a similar idea to sample approximately from unnormalized probability density functions and estimate their normalizing constants. We consider a process where the target density diffuses towards a Gaussian. Denoising Diffusion Samplers (DDS) are obtained by approximating the corresponding time-reversal. While score matching is not applicable in this context, we can leverage many of the ideas introduced in generative modeling for Monte Carlo sampling. Existing theoretical results from denoising diffusion models also provide theoretical guarantees for DDS. We discuss the connections between DDS, optimal control and Schrödinger bridges and finally demonstrate DDS experimentally on a variety of challenging sampling tasks. . Quantum ground states from reinforcement learning. In Mathematical and Scientific Machine Learning, 2020.Alessandro Beghi. On the relative entropy of discrete-time Markov processes with given end-point densities. . Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems, 2020.Emiel Hoogeboom and Tim Salimans. Blurring diffusion models. arXiv preprint arXiv:2209.05557, 2022.Aapo Hyvärinen. Estimation of non-normalized statistical models by score matching. | Published as a conference paper at ICLR 2023 DENOISING DIFFUSION SAMPLERS |
d17280075 | Two potential bottlenecks on the expressiveness of recurrent neural networks (RNNs) are their ability to store information about the task in their parameters, and to store information about the input history in their units. We show experimentally that all common RNN architectures achieve nearly the same per-task and per-unit capacity bounds with careful training, for a variety of tasks and stacking depths. They can store an amount of task information which is linear in the number of parameters, and is approximately 5 bits per parameter. They can additionally store approximately one real number from their input history per hidden unit. We further find that for several tasks it is the per-task parameter capacity bound that determines performance. These results suggest that many previous results comparing RNN architectures are driven primarily by differences in training effectiveness, rather than differences in capacity. Supporting this observation, we compare training difficulty for several architectures, and show that vanilla RNNs are far more difficult to train, yet have slightly higher capacity. Finally, we propose two novel RNN architectures, one of which is easier to train than the LSTM or GRU for deeply stacked architectures. * Work done as a member of the Google Brain Residency program (g.co/brainresidency).Published as a conference paper at ICLR 2017 (per-parameter and per-unit), as well as the results of trainability experiments (training on extremely hard tasks where gated models might reasonably be expected to perform better).CAPACITY BOTTLENECKSThere are several potential bottlenecks for RNNs, for example: How much information about the task can they store in their parameters? How much information about the input history can they store in their units? These first two bottlenecks can both be seen as memory capacities (one for the task, one for the inputs), for different types of memory.Another, different kind of capacity stems from the set of computational primitives an RNN is able to perform. For example, maybe one wants to multiply two numbers. In terms of number of units and time steps, this task may be very straight-forward using some specific computational primitives and dynamics, but with others it may be extremely resource heavy. One might expect that differences in computational capacity due to different computational primitives would play a large role in performance. However, despite the fact that the gated architectures are outfitted with a multiplicative primitive between hidden units, while the vanilla RNN is not, we found no evidence of a computational bottleneck in our experiments. We therefore will focus only on the per-parameter capacity of an RNN to learn about its task during training, and on the per-unit memory capacity of an RNN to remember its inputs.EXPERIMENTAL SETUP | Published as a conference paper at ICLR 2017 CAPACITY AND TRAINABILITY IN RECURRENT NEURAL NETWORKS |
d29842525 | Topic models are one of the most popular methods for learning representations of text, but a major challenge is that any change to the topic model requires mathematically deriving a new inference algorithm. A promising approach to address this problem is autoencoding variational Bayes (AEVB), but it has proven difficult to apply to topic models in practice. We present what is to our knowledge the first effective AEVB based inference method for latent Dirichlet allocation (LDA), which we call Autoencoded Variational Inference For Topic Model (AVITM). This model tackles the problems caused for AEVB by the Dirichlet prior and by component collapsing. We find that AVITM matches traditional methods in accuracy with much better inference time. Indeed, because of the inference network, we find that it is unnecessary to pay the computational cost of running variational optimization on test data. Because AVITM is black box, it is readily applied to new topic models. As a dramatic illustration of this, we present a new topic model called ProdLDA, that replaces the mixture model in LDA with a product of experts. By changing only one line of code from LDA, we find that ProdLDA yields much more interpretable topics, even if LDA is trained via collapsed Gibbs sampling. | Published as a conference paper at ICLR 2017 AUTOENCODING VARIATIONAL INFERENCE FOR TOPIC MODELS |
d246430723 | The exponential growth in numbers of parameters of neural networks over the past years has been accompanied by an increase in performance across several fields. However, due to their sheer size, the networks not only became difficult to interpret but also problematic to train and use in real-world applications, since hardware requirements increased accordingly. Tackling both issues, we present a novel approach that either drops a neural network's initial weights or inverts their respective sign. Put simply, a network is trained by weight selection and inversion without changing their absolute values. Our contribution extends previous work on masking by additionally sign-inverting the initial weights and follows the findings of the Lottery Ticket Hypothesis. Through this extension and adaptations of initialization methods, we achieve a pruning rate of up to 99%, while still matching or exceeding the performance of various baseline and previous models. Our approach has two main advantages. First, and most notable, signed Supermask models drastically simplify a model's structure, while still performing well on given tasks. Second, by reducing the neural network to its very foundation, we gain insights into which weights matter for performance. The code is available here.Published as a conference paper at ICLR 2022Zhou et al. (2019)follow the seminal idea of the LTH and are able to train neural networks by only selecting untrained weights (i.e., weights are frozen after initialization), a concept they call Supermasks. In other words, they find a smaller subnetwork during training without adjusting the weights themselves. Although this approach did not match the performance of their baselines and the pruning rate is inconsistent, it revealed a startling insight: weight values do not seem to be as important as the connection itself; a single, well-initialized value for each layer is sufficient. Ramanujan et al. (2020) further develop Supermasks by modifying the way masks are calculated, which leads to significant performance improvements compared to Zhou et al. (2019). However, the number of parameters is still high. Recently, Chijiwa et al. (2021) modified the approach of Ramanujan et al. (2020) by randomizing the scores, but prune their networks only up to 60%. In this paper, we propose a technique called signed Supermask, a natural extension of Zhou et al. (2019) and Ramanujan et al. (2020). We not only determine the importance of a weight by masking, but also learn the respective sign of a weight. Signed Supermasks aim at very sparse structures and are able to uncover subnetworks with far fewer parameters in the range of 0.5% to 4% of the original network requiring little additional computational effort without sacrificing performance.This differs substantially from low precision training. In its most extreme form, low-precision training quantizes the weight values of a neural network to three constants, including zero or two constants, excluding zero. There, binarized neural networks (BNN) (Courbariaux et al., 2015) reduce complexity but not the size of the networks in terms of sparsity of weights. Ternary neural networks (TNN), introduced by Li et al. (2016a) allow in principle for sparse subnetworks, however this literature focuses almost exclusively on optimizing computational costs while maintaining predictive performance. For TNNs to reduce computational complexity, sparsity (and interpretability) is of no importance, as they are focused on reducing the computational footprint only. The literature presents diverse approaches, for example Shayer et al. (2017) utilize the local reparameterization trick (Kingma et al., 2015) to learn ternarized weights stochastically, Zhu et al. (2016) ternarize the weights but scale them layer-wise by two learned real-valued scalars and Alemdar et al. (2016) employ a teacher-student approach. Deng & Zhang (2020) train their networks normally with an additional regularization term in the loss function and only ternarize at the end of training by rounding. To the best of our knowledge, this is the only work on TNNs also reporting on sparsity. More recently, Diffenderfer & Kailkhura (2021) combined the edge-popup algorithm byRamanujan et al. (2020)and the binarization of weights and achieve good results. Thus, although TNNs and Supermasks in general appear similar at first glance, their goals are different: while the former attempt to reduce computational complexity, the latter attempt to find the smallest possible subnetworks within a neural network to better understand neural networks in general and work towards facilitating interpretability. This paper takes the Supermasks perspective and our experiments show, that signing the Supermask matches or outperforms baselines and state-of-the-art approaches on Supermasks and leads to very sparse representations. A convenient side effect of signed Supermask is the ternarization of weights, with implied reduced memory requirements (Li et al., 2016a) and further significant speedup in inference (Hidayetoglu et al., 2020;Brasoveanu et al., 2020). In summary, signed Supermasks provide two major advantages. First, the network structure is simplified while maintaining or improving the performance compared to their dense counterpart. Second, the reduction facilitates a better understanding of the inner mechanics of neural networks. Based on that, we might be able to build smaller but equally powerful models a priori. Additionally, once trained, signed Supermask models can be stored more efficiently and have the potential for faster inference. Jonathan Frankle and Michael Carbin. The lottery ticket hypothesis: Finding sparse, trainable neural networks. 3 2018. URL http://arxiv.org/abs/1803.03635. Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, and Michael Carbin. Stabilizing the lottery ticket hypothesis. 3 2019. URL http://arxiv.org/abs/1903.01611. Xavier Glorot and Yoshua Bengio. Understanding the difficulty of training deep feedforward neural networks. . Dally. Learning both weights and connections for efficient neural networks. 6 2015. URL http://arxiv.org/abs/1506.02626. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. 2 2015. URL http://arxiv.org/abs/ 1502.01852. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. . At-scale sparse deep neural network inference with efficient gpu implementation. | Published as a conference paper at ICLR 2022 SIGNING THE SUPERMASK: KEEP, HIDE, INVERT |
d16231549 | Suitable lateral connections between encoder and decoder are shown to allow higher layers of a denoising autoencoder (dAE) to focus on invariant representations. In regular autoencoders, detailed information needs to be carried through the highest layers but lateral connections from encoder to decoder relieve this pressure. It is shown that abstract invariant features can be translated to detailed reconstructions when invariant features are allowed to modulate the strength of the lateral connection. Three dAE structures with modulated and additive lateral connections, and without lateral connections were compared in experiments using real-world images. The experiments verify that adding modulated lateral connections to the model 1) improves the accuracy of the probability model for inputs, as measured by denoising performance; 2) results in representations whose degree of invariance grows faster towards the higher layers; and 3) supports the formation of diverse invariant poolings. | Under review as a conference paper at ICLR 2015 DENOISING AUTOENCODER WITH MODULATED LATERAL CONNECTIONS LEARNS INVARIANT REPRESENTATIONS OF NATURAL IMAGES |
d8737624 | In this paper, we propose a framework for training multiple neural networks simultaneously. The parameters from all models are regularised by the tensor trace norm, so that one neural network is encouraged to reuse others' parameters if possible -this is the main motivation behind multi-task learning. In contrast to many deep multi-task learning work, we do not predefine a parameter sharing strategy by tying some (usually bottom) layers' parameters, instead, our framework allows the sharing for all shareable layers thus the sharing strategy is learned from a pure data-driven way. | Trace Norm Regularised Deep Multi-Task Learning |
d249538418 | The Strong Lottery Ticket Hypothesis (SLTH) stipulates the existence of a subnetwork within a sufficiently overparameterized (dense) neural network that-when initialized randomly and without any training-achieves the accuracy of a fully trained target network. Recent works by da Cunha et al. (2022b); Burkholz (2022a) demonstrate that the SLTH can be extended to translation equivariant networks-i.e. CNNs-with the same level of overparametrization as needed for the SLTs in dense networks. However, modern neural networks are capable of incorporating more than just translation symmetry, and developing general equivariant architectures such as rotation and permutation has been a powerful design principle. In this paper, we generalize the SLTH to functions that preserve the action of the group G-i.e. G-equivariant network-and prove, with high probability, that one can approximate any G-equivariant network of fixed width and depth by pruning a randomly initialized overparametrized G-equivariant network to a G-equivariant subnetwork. We further prove that our prescribed overparametrization scheme is optimal and provides a lower bound on the number of effective parameters as a function of the error tolerance. We develop our theory for a large range of groups, including subgroups of the Euclidean E(2) and Symmetric group G ≤ S n -allowing us to find SLTs for MLPs, CNNs, E(2)-steerable CNNs, and permutation equivariant networks as specific instantiations of our unified framework. Empirically, we verify our theory by pruning overparametrized E(2)-steerable CNNs, k-order GNNs, and message passing GNNs to match the performance of trained target networks. * denotes equal contribution. † Published as a conference paper at ICLR 2023 strong lottery ticket hypothesis (SLTH) was proven for overparametrized dense networks with no biases (Malach et al., 2020; Pensia et al., 2020;Orseau et al., 2020), non-zero biases(Fischer and Burkholz, 2021), and vanilla CNNs (da Cunha et al., 2022b). Recently, Burkholz (2022b) extended the work of Pensia et al. (2020) to most activation functions that behave like ReLU around the origin, and adopted another overparametrization framework as in Pensia et al. (2020) such that the overparametrized network has depth L + 1 (no longer 2L). However, the optimality with respect to the number of parameters (Theorem 2 in Pensia et al.(2020)) is lost with this method. Moreover, Burkholz (2022a) extended the results of da Cunha et al. (2022b) on CNNs to non-positive inputs.Modern architectures, however, are more than just MLPs and CNNs and many encode data-dependent inductive biases in the form of equivariances and invariances that are pivotal to learning smaller and more efficient networks(He et al., 2021). This raises the important question: can we simultaneously get the benefits of equivariance and pruning? In other words, does there exist winning tickets for the equivariant strong lottery for general equivariant networks given sufficient overparametrization?Present Work. In this paper, we develop a unifying framework to study and prove the existence of strong lottery tickets (SLTs) for general equivariant networks. Specifically, in our main result (Thm. 1) we prove that any fixed width and depth target G-equivariant network that uses a point-wise ReLU can be approximated with high probability to a pre-specified tolerance by a subnetwork within a random G-equivariant network that is overparametrized by doubling the depth and increasing the width by a logarithmic factor. Such a theorem allows us to immediately recover the results of Pensia et al.(2020); Orseau et al. (2020) for MLPs and of Burkholz et al. (2022); da Cunha et al. (2022b)for CNNs as specific instantiations under our unified equivariant framework. Furthermore, we prove that a logarithmic overparametrization is necessarily optimal-by providing a lower bound in Thm. 2-as a function of the tolerance. Crucially, this is irrespective of which overparametrization strategy is employed which demonstrates the optimality of Theorem 1. Notably, the extracted subnetwork is also G-equivariant, preserving the desirable inductive biases of the target model; such a fact is importantly not achievable via a simple application of previous results found in (Pensia et al., 2020; da Cunha et al., 2022b).Our theory is broadly applicable to any equivariant network that uses a pointwise ReLU nonlinearity. This includes the popular E(2)-steerable CNNs with regular representations (Weiler and Cesa, 2019) (Corollary 1) that model symmetries of the 2d-plane as well as subgroups of the symmetric group of n elements S n , allowing us to find SLTs for permutation equivariant networks (Corollary 2) as a specific instantiation. We substantiate our theory by conducting experiments by explicitly computing the pruning masks for randomly initialized overparametrized E(2)-steerable networks, k-order GNNs, and MPGNNs to approximate another fully trained target equivariant network. | A GENERAL FRAMEWORK FOR PROVING THE EQUIV- ARIANT STRONG LOTTERY TICKET HYPOTHESIS |
d249394837 | Long-term engagement is preferred over immediate engagement in sequential recommendation as it directly affects product operational metrics such as daily active users (DAUs) and dwell time. Meanwhile, reinforcement learning (RL) is widely regarded as a promising framework for optimizing long-term engagement in sequential recommendation. However, due to expensive online interactions, it is very difficult for RL algorithms to perform state-action value estimation, exploration and feature extraction when optimizing long-term engagement. In this paper, we propose ResAct which seeks a policy that is close to, but better than, the online-serving policy. In this way, we can collect sufficient data near the learned policy so that state-action values can be properly estimated, and there is no need to perform online interaction. ResAct optimizes the policy by first reconstructing the online behaviors and then improving it via a Residual Actor. To extract long-term information, ResAct utilizes two information-theoretical regularizers to confirm the expressiveness and conciseness of features. We conduct experiments on a benchmark dataset and a large-scale industrial dataset which consists of tens of millions of recommendation requests. Experimental results show that our method significantly outperforms the state-of-the-art baselines in various long-term engagement optimization tasks. * The work was done during an internship at Kuaishou Technology. | Published as a conference paper at ICLR 2023 RESACT: REINFORCING LONG-TERM ENGAGEMENT IN SEQUENTIAL RECOMMENDATION WITH RESIDUAL ACTOR |
d604334 | Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties. First, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains the semantic information in the high layers of neural networks. Second, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extent. We can cause the network to misclassify an image by applying a certain hardly perceptible perturbation, which is found by maximizing the network's prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input. | Intriguing properties of neural networks |
d222125277 | Entities are at the center of how we represent and aggregate knowledge. For instance, Encyclopedias such as Wikipedia are structured by entities (e.g., one per Wikipedia article). The ability to retrieve such entities given a query is fundamental for knowledge-intensive tasks such as entity linking and open-domain question answering. One way to understand current approaches is as classifiers among atomic labels, one for each entity. Their weight vectors are dense entity representations produced by encoding entity meta information such as their descriptions. This approach leads to several shortcomings: (i) context and entity affinity is mainly captured through a vector dot product, potentially missing fine-grained interactions between the two; (ii) a large memory footprint is needed to store dense representations when considering large entity sets; (iii) an appropriately hard set of negative data has to be subsampled at training time. In this work, we propose GENRE, the first system that retrieves entities by generating their unique names, left to right, token-by-token in an autoregressive fashion and conditioned on the context. This enables us to mitigate the aforementioned technical issues since: (i) the autoregressive formulation allows us to directly capture relations between context and entity name, effectively cross encoding both; (ii) the memory footprint is greatly reduced because the parameters of our encoder-decoder architecture scale with vocabulary size, not entity count; (iii) the exact softmax loss can be efficiently computed without the need to subsample negative data. We show the efficacy of the approach, experimenting with more than 20 datasets on entity disambiguation, end-to-end entity linking and document retrieval tasks, achieving new state-of-the-art or very competitive results while using a tiny fraction of the memory footprint of competing systems. Finally, we demonstrate that new entities can be added by simply specifying their unambiguous name. * Work done during internship with Facebook AI Research. | Under review AUTOREGRESSIVE ENTITY RETRIEVAL |
d201668203 | Short-and-sparse deconvolution (SaSD) is the problem of extracting localized, recurring motifs in signals with spatial or temporal structure. Variants of this problem arise in applications such as image deblurring, microscopy, neural spike sorting, and more. The problem is challenging in both theory and practice, as natural optimization formulations are nonconvex. Moreover, practical deconvolution problems involve smooth motifs (kernels) whose spectra decay rapidly, resulting in poor conditioning and numerical challenges. This paper is motivated by recent theoretical advances [ZLK`17, KZLW19], which characterize the optimization landscape of a particular nonconvex formulation of SaSD. This is used to derive a provable algorithm which exactly solves certain non-practical instances of the SaSD problem. We leverage the key ideas from this theory (sphere constraints, data-driven initialization) to develop a practical algorithm, which performs well on data arising from a range of application areas. We highlight key additional challenges posed by the ill-conditioning of real SaSD problems, and suggest heuristics (acceleration, continuation, reweighting) to mitigate them. Experiments demonstrate both the performance and generality of the proposed method.Index termssparse blind deconvolution, convolutional dictionary learning, computational imaging, nonconvex optimization, alternating descent methods. | Short-and-Sparse Deconvolution -A Geometric Approach |
d12730344 | Catastrophic forgetting is a problem faced by many machine learning models and algorithms. When trained on one task, then trained on a second task, many machine learning models "forget" how to perform the first task. This is widely believed to be a serious problem for neural networks. Here, we investigate the extent to which the catastrophic forgetting problem occurs for modern neural networks, comparing both established and recent gradient-based training algorithms and activation functions. We also examine the effect of the relationship between the first task and the second task on catastrophic forgetting. We find that it is always best to train using the dropout algorithmthe dropout algorithm is consistently best at adapting to the new task, remembering the old task, and has the best tradeoff curve between these two extremes. We find that different tasks and relationships between tasks result in very different rankings of activation function performance. This suggests that the choice of activation function should always be cross-validated. | An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks |
d166228758 | We focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. The architecture has a number of desirable properties, being interpretable, applicable without modification to a wide array of target domains, and fast to train. We test the proposed architecture on several well-known datasets, including M3, M4 and TOURISM competition datasets containing time series from diverse domains. We demonstrate state-of-the-art performance for two configurations of N-BEATS for all the datasets, improving forecast accuracy by 11% over a statistical benchmark and by 3% over last year's winner of the M4 competition, a domain-adjusted hand-crafted hybrid between neural network and statistical time series models. The first configuration of our model does not employ any time-series-specific components and its performance on heterogeneous datasets strongly suggests that, contrarily to received wisdom, deep learning primitives such as residual blocks are by themselves sufficient to solve a wide range of forecasting problems. Finally, we demonstrate how the proposed architecture can be augmented to provide outputs that are interpretable without considerable loss in accuracy.Published as a conference paper at ICLR 2020 inject a suitable inductive bias in the model to make its internal operations more interpretable, in the sense of extracting some explainable driving factors combining to produce a given forecast?SUMMARY OF CONTRIBUTIONSDeep Neural Architecture: To the best of our knowledge, this is the first work to empirically demonstrate that pure DL using no time-series specific components outperforms well-established statistical approaches on M3, M4 and TOURISM datasets (on M4, by 11% over statistical benchmark, by 7% over the best statistical entry, and by 3% over the M4 competition winner). In our view, this provides a long-missing proof of concept for the use of pure ML in TS forecasting and strengthens motivation to continue advancing the research in this area.Interpretable DL for Time Series: In addition to accuracy benefits, we also show that it is feasible to design an architecture with interpretable outputs that can be used by practitioners in very much the same way as traditional decomposition techniques such as the "seasonality-trend-level" approach (Cleveland et al., 1990).V. Assimakopoulos and K. Nikolopoulos. The theta model: a decomposition approach to forecasting. | Published as a conference paper at ICLR 2020 N-BEATS: NEURAL BASIS EXPANSION ANALYSIS FOR INTERPRETABLE TIME SERIES FORECASTING |
d15654042 | In this work we study the properties of deep neural networks with random weights. We formally prove that these networks perform a distance-preserving embedding of the data. Based on this we then draw conclusions on the size of the training data and the networks' structure. | ON THE STABILITY OF DEEP NETWORKS |
d22090507 | We introduce a new audio processing technique that increases the sampling rate of signals such as speech or music using deep convolutional neural networks. Our model is trained on pairs of low and high-quality audio examples; at test-time, it predicts missing samples within a low-resolution signal in an interpolation process similar to image super-resolution. Our method is simple and does not involve specialized audio processing techniques; in our experiments, it outperforms baselines on standard speech and music benchmarks at upscaling ratios of 2×, 4×, and 6×. The method has practical applications in telephony, compression, and text-tospeech generation; it demonstrates the effectiveness of convolutional architectures on an audio generation task. | Workshop track -ICLR 2017 AUDIO SUPER-RESOLUTION USING NEURAL NETS |
d249097686 | Real-world applications require the classification model to adapt to new classes without forgetting old ones. Correspondingly, Class-Incremental Learning (CIL) aims to train a model with limited memory size to meet this requirement. Typical CIL methods tend to save representative exemplars from former classes to resist forgetting, while recent works find that storing models from history can substantially boost the performance. However, the stored models are not counted into the memory budget, which implicitly results in unfair comparisons. We find that when counting the model size into the total budget and comparing methods with aligned memory size, saving models do not consistently work, especially for the case with limited memory budgets. As a result, we need to holistically evaluate different CIL methods at different memory scales and simultaneously consider accuracy and memory size for measurement. On the other hand, we dive deeply into the construction of the memory buffer for memory efficiency. By analyzing the effect of different layers in the network, we find that shallow and deep layers have different characteristics in CIL. Motivated by this, we propose a simple yet effective baseline, denoted as MEMO for Memory-efficient Expandable MOdel. MEMO extends specialized layers based on the shared generalized representations, efficiently extracting diverse representations with modest cost and maintaining representative exemplars. Extensive experiments on benchmark datasets validate MEMO's competitive performance. | A MODEL OR 603 EXEMPLARS: TOWARDS MEMORY- EFFICIENT CLASS-INCREMENTAL LEARNING |
d231662264 | Batch Normalization is a key component in almost all state-of-the-art image classifiers, but it also introduces practical challenges: it breaks the independence between training examples within a batch, can incur compute and memory overhead, and often results in unexpected bugs. Building on recent theoretical analyses of deep ResNets at initialization, we propose a simple set of analysis tools to characterize signal propagation on the forward pass, and leverage these tools to design highly performant ResNets without activation normalization layers. Crucial to our success is an adapted version of the recently proposed Weight Standardization. Our analysis tools show how this technique preserves the signal in networks with ReLU or Swish activation functions by ensuring that the per-channel activation means do not grow with depth. Across a range of FLOP budgets, our networks attain performance competitive with the state-of-the-art EfficientNets on ImageNet.Published as a conference paper at ICLR 2021 works. Leveraging these SPPs, we show how to design unnormalized ResNets which are constrained to have signal propagation properties similar to batch-normalized ResNets.• We identify a key failure mode in unnormalized ResNets with ReLU or Swish activations and Gaussian weights. Because the mean output of these non-linearities is positive, the squared mean of the hidden activations on each channel grows rapidly as the network depth increases. To resolve this, we propose Scaled Weight Standardization, a minor modification of the recently proposed Weight Standardization (Qiao et al., 2019; Huang et al., 2017b), which prevents the growth in the mean signal, leading to a substantial boost in performance.• We apply our normalization-free network structure in conjunction with Scaled Weight Standardization to ResNets on ImageNet, where we for the first time attain performance which is comparable or better than batch-normalized ResNets on networks as deep as 288 layers.• Finally, we apply our normalization-free approach to the RegNet architecture (Radosavovic et al., 2020). By combining this architecture with the compound scaling strategy proposed byTan& Le (2019), we develop a class of models without normalization layers which are competitive with the current ImageNet state of the art across a range of FLOP budgets. . Shufflenet v2: Practical guidelines for efficient cnn architecture design. In . Spectral normalization for generative adversarial networks. In ICLR, 2018. Y. Nesterov. A method for unconstrained convex minimization problem with the rate of convergence O(1/k 2 ). Doklady AN USSR, pp. (269), 543-547, 1983. Art B Owen. A robust hybrid of lasso and ridge regression. 2007. . ImageNet large scale visual recognition challenge. IJCV, 115:211-252, 2015. Tim Salimans and Durk P Kingma. Weight normalization: A simple reparameterization to accelerate training of deep neural networks. In Advances in neural information processing systems, pp. 901-909, 2016. | Published as a conference paper at ICLR 2021 CHARACTERIZING SIGNAL PROPAGATION TO CLOSE THE PERFORMANCE GAP IN UNNORMALIZED RESNETS |
d4394853 | Workshop track -ICLR 2018 META-LEARNING A DYNAMICAL LANGUAGE MODEL | |
d251554799 | Antibody design is valuable for therapeutic usage and biological research. Existing deep-learning-based methods encounter several key issues: 1) incomplete context for Complementarity-Determining Regions (CDRs) generation; 2) incapability of capturing the entire 3D geometry of the input structure; 3) inefficient prediction of the CDR sequences in an autoregressive manner. In this paper, we propose Multi-channel Equivariant Attention Network (MEAN) to co-design 1D sequences and 3D structures of CDRs. To be specific, MEAN formulates antibody design as a conditional graph translation problem by importing extra components including the target antigen and the light chain of the antibody. Then, MEAN resorts to E(3)-equivariant message passing along with a proposed attention mechanism to better capture the geometrical correlation between different components. Finally, it outputs both the 1D sequences and 3D structure via a multi-round progressive full-shot scheme, which enjoys more efficiency and precision against previous autoregressive approaches. Our method significantly surpasses state-of-theart models in sequence and structure modeling, antigen-binding CDR design, and binding affinity optimization. Specifically, the relative improvement to baselines is about 23% in antigen-binding CDR design and 34% for affinity optimization. | Published as a conference paper at ICLR 2023 CONDITIONAL ANTIBODY DESIGN AS 3D EQUIVARI- ANT GRAPH TRANSLATION |
d257482844 | State-of-the-art computer vision models are mostly trained with supervised learning using human-labeled images, which limits their scalability due to the expensive annotation cost. While self-supervised representation learning has achieved impressive progress, it still requires a second stage of finetuning on labeled data. On the other hand, models pre-trained with large-scale text-image supervision (e.g., CLIP) have enabled zero-shot transfer to downstream image classification tasks. However, the zero-shot performance of CLIP-like models are often insufficient for real-world adoption. In this paper, we aim to leverage the abundant unlabeled data from a target domain to improve the performance of a pre-trained zero-shot classifier, by unsupervised finetuning of the pre-trained model. We propose Masked Unsupervised Self-Training (MUST), a new unsupervised adaptation method which leverages two different and complementary sources of training signals: pseudo-labels and raw images. MUST jointly optimizes three objectives to learn both class-level global feature and pixel-level local feature and enforces a regularization between the two. We demonstrate the efficacy of MUST on a variety of downstream tasks, where it improves upon CLIP by a large margin. MUST also outperforms supervised fewshot adaptation methods. It achieves a top-1 accuracy of 77.7% on ImageNet using ViT-B, +9.4% higher than CLIP, and +6.2% higher than 16-shot CLIP adaptation. Our code is available at https://github.com/salesforce/MUST. | Published as a conference paper at ICLR 2023 MASKED UNSUPERVISED SELF-TRAINING FOR LABEL- FREE IMAGE CLASSIFICATION |
d16926563 | We propose a framework for detecting action patterns from motion sequences and modeling the sensory-motor relationship of animals, using a generative recurrent neural network. The network has a discriminative part (classifying actions) and a generative part (predicting motion), whose recurrent cells are laterally connected, allowing higher levels of the network to represent high level behavioral phenomena. We test our framework on two types of data, fruit fly behavior and online handwriting. Our results show that 1) taking advantage of unlabeled sequences, by predicting future motion, significantly improves action detection performance when training labels are scarce, 2) the network learns to represent high level phenomena such as writer identity and fly gender, without supervision, and 3) simulated motion trajectories, generated by treating motion prediction as input to the network, look realistic and may be used to qualitatively evaluate whether the model has learnt generative control rules. | Under review as a conference paper at ICLR 2017 LEARNING RECURRENT REPRESENTATIONS FOR HIERARCHICAL BEHAVIOR MODELING |
d211068987 | Autoencoder reconstructions are widely used for the task of unsupervised anomaly localization. Indeed, an autoencoder trained on normal data is expected to only be able to reconstruct normal features of the data, allowing the segmentation of anomalous pixels in an image via a simple comparison between the image and its autoencoder reconstruction. In practice however, local defects added to a normal image can deteriorate the whole reconstruction, making this segmentation challenging. To tackle the issue, we propose in this paper a new approach for projecting anomalous data on a autoencoder-learned normal data manifold, by using gradient descent on an energy derived from the autoencoder's loss function. This energy can be augmented with regularization terms that model priors on what constitutes the user-defined optimal projection. By iteratively updating the input of the autoencoder, we bypass the loss of high-frequency information caused by the autoencoder bottleneck. This allows to produce images of higher quality than classic reconstructions. Our method achieves state-of-the-art results on various anomaly localization datasets. It also shows promising results at an inpainting task on the CelebA dataset. * Equal contributions. | ITERATIVE ENERGY-BASED PROJECTION ON A NOR- MAL DATA MANIFOLD FOR ANOMALY LOCALIZATION |
d237303776 | Exploration remains a central challenge for reinforcement learning (RL). Virtually all existing methods share the feature of a monolithic behaviour policy that changes only gradually (at best). In contrast, the exploratory behaviours of animals and humans exhibit a rich diversity, namely including forms of switching between modes. This paper presents an initial study of mode-switching, nonmonolithic exploration for RL. We investigate different modes to switch between, at what timescales it makes sense to switch, and what signals make for good switching triggers. We also propose practical algorithmic components that make the switching mechanism adaptive and robust, which enables flexibility without an accompanying hyper-parameter-tuning burden. Finally, we report a promising and detailed analysis on Atari, using two-mode exploration and switching at sub-episodic time-scales. | Published as a conference paper at ICLR 2022 WHEN SHOULD AGENTS EXPLORE? |
d2239496 | A long-term goal of machine learning is to build intelligent conversational agents. One recent popular approach is to train end-to-end models on a large amount of real dialog transcripts between humans(Sordoni et al., 2015;Vinyals & Le, 2015;Shang et al., 2015). However, this approach leaves many questions unanswered as an understanding of the precise successes and shortcomings of each model is hard to assess. A contrasting recent proposal are the bAbI tasks(Weston et al., 2015b)which are synthetic data that measure the ability of learning machines at various reasoning tasks over toy language. Unfortunately, those tests are very small and hence may encourage methods that do not scale. In this work, we propose a suite of new tasks of a much larger scale that attempt to bridge the gap between the two regimes. Choosing the domain of movies, we provide tasks that test the ability of models to answer factual questions (utilizing OMDB), provide personalization (utilizing MovieLens), carry short conversations about the two, and finally to perform on natural dialogs from Reddit. We provide a dataset covering ∼75k movie entities and with ∼3.5M training examples. We present results of various models on these tasks, and evaluate their performance. * The first three authors contributed equally. | EVALUATING PREREQUISITE QUALITIES FOR LEARN- ING END-TO-END DIALOG SYSTEMS |
d235313383 | We propose the convergent graph solver (CGS) 1 , a deep learning method that learns iterative mappings to predict the properties of a graph system at its stationary state (fixed point) with guaranteed convergence. The forward propagation of CGS proceeds in three steps: (1) constructing the input-dependent linear contracting iterative maps, (2) computing the fixed points of the iterative maps, and (3) decoding the fixed points to estimate the properties. The contractivity of the constructed linear maps guarantees the existence and uniqueness of the fixed points following the Banach fixed point theorem. To train CGS efficiently, we also derive a tractable analytical expression for its gradient by leveraging the implicit function theorem. We evaluate the performance of CGS by applying it to various network-analytic and graph benchmark problems. The results indicate that CGS has competitive capabilities for predicting the stationary properties of graph systems, irrespective of whether the target systems are linear or non-linear. CGS also shows high performance for graph classification problems where the existence or the meaning of a fixed point is hard to be clearly defined, which highlights the potential of CGS as a general graph neural network architecture.Published as a conference paper at ICLR 2022In this study, we propose a convergent graph solver (CGS), a deep learning method that can predict the solution of a target graph analytical problem using only the input and output data, and without requiring the prior knowledge of existing solvers or intermediate solutions. The forward propagation of CGS is designed to proceed in the following three steps:• Constructing the input-dependent linear-contracting iterative maps. CGS uses the input graph, which dictates the specification of the target network-analytic problem, to construct a set of linear contracting maps. This procedure formulates/set up the internal problem to be solved by considering the problem conditions and contexts (i.e., boundary conditions or initial conditions in PDE domains -the physical network problems). Furthermore, the input-dependent linear map can produce any size of transition map flexibly depending on the input size graph; thus helping the trained model to generalize over unseen problems with different sizes (size transferability).• Computing the fixed points via iterative methods. CGS constructs a set of linear contracting maps, each of which is guaranteed to have a unique fixed point that embeds the important features for conducting various end tasks. Thus, CGS computes the unique solutions of the constructed linear maps via iterative methods (or direct inversion) with convergence guarantee.Published as a conference paper at ICLR 2022 Claudio Gallicchio and Alessio Micheli. Fast and deep graph neural networks. In Guo. On differentiating parameterized argmin and argmax problems with application to bi-level optimization. arXiv preprint arXiv:1607.05447, 2016. | Published as a conference paper at ICLR 2022 CONVERGENT GRAPH SOLVERS |
d231847140 | In novel class discovery (NCD), we are given labeled data from seen classes and unlabeled data from unseen classes, and we train clustering models for the unseen classes. However, the implicit assumptions behind NCD are still unclear. In this paper, we demystify assumptions behind NCD and find that high-level semantic features should be shared among the seen and unseen classes. Based on this finding, NCD is theoretically solvable under certain assumptions and can be naturally linked to meta-learning that has exactly the same assumption as NCD. Thus, we can empirically solve the NCD problem by meta-learning algorithms after slight modifications. This meta-learning-based methodology significantly reduces the amount of unlabeled data needed for training and makes it more practical, as demonstrated in experiments.Horse BirdCat Fish (b) Experts sample data (sampling in causality).Figure 1: NCD aims to discover novel classes (i.e., clustering novel-class data) with the help of labeled known-class data. There exists two ways to obtain data in NCD: (a) labeling in causality, e.g., we first obtain unlabeled images and then hire experts to label them and (b) sampling in causality, e.g., we are given a label set, and then sample images regarding these labels. In (a), experts have to go through all images and find out novel classes. However, the novel classes (like cars) might be totally different from known classes (like animals), which makes NCD become a theoretically unsolvable problem. In this paper, we revisit NCD from (b), where novel-class data are collected on the same way of sampling known-class data. In this view, NCD can be theoretically solved, since novel classes and known classes are highly related. The yellow rectangles represent the identified novel classes. . What can be transferred: Unsupervised domain adaptation for endoscopic lesions segmentation. In CVPR, 2020. Jiahua Dong, Yang Cong, Gan Sun, Zhen Fang, and Zhengming Ding. Where and how to transfer: Knowledge aggregation-induced transferability perception for. unsupervised domain adaptation. . Open set domain adaptation: Theoretical bound and algorithm. | Published as a conference paper at ICLR 2022 META DISCOVERY: LEARNING TO DISCOVER NOVEL CLASSES GIVEN VERY LIMITED DATA |
d15659468 | Partition functions arise in a variety of settings, including conditional random fields, logistic regression, and latent gaussian models. In this paper, we consider semistochastic quadratic bound (SQB) methods for maximum likelihood estimation based on partition function optimization. Batch methods based on the quadratic bound were recently proposed for this class of problems, and performed favorably in comparison to state-of-the-art techniques. Semistochastic methods fall in between batch algorithms, which use all the data, and stochastic gradient type methods, which use small random selections at each iteration. We build semistochastic quadratic bound-based methods, and prove both global convergence (to a stationary point) under very weak assumptions, and linear convergence rate under stronger assumptions on the objective. To make the proposed methods faster and more stable, we consider inexact subproblem minimization and batchsize selection schemes. The efficacy of SQB methods is demonstrated via comparison with several state-of-the-art techniques on commonly used datasets. | Semistochastic quadratic bound methods |
d231800078 | Deep generative adversarial networks (GANs) have gained growing popularity in numerous scenarios, while usually suffer from high parameter complexities for resource-constrained real-world applications. However, the compression of GANs has less been explored. A few works show that heuristically applying compression techniques normally leads to unsatisfactory results, due to the notorious training instability of GANs. In parallel, the lottery ticket hypothesis shows prevailing success on discriminative models, in locating sparse matching subnetworks capable of training in isolation to full model performance. In this work, we for the first time study the existence of such trainable matching subnetworks in deep GANs. For a range of GANs, we certainly find matching subnetworks at 67%-74% sparsity. We observe that with or without pruning discriminator has a minor effect on the existence and quality of matching subnetworks, while the initialization weights used in the discriminator plays a significant role. We then show the powerful transferability of these subnetworks to unseen tasks. Furthermore, extensive experimental results demonstrate that our found subnetworks substantially outperform previous state-of-the-art GAN compression approaches in both image generation (e.g. SNGAN) and image-to-image translation GANs (e.g. CycleGAN). Codes available at https://github.com/VITA-Group/GAN-LTH. | Published as a conference paper at ICLR 2021 GANS CAN PLAY LOTTERY TICKETS TOO |
d214802067 | We formalize an equivalence between two popular methods for Bayesian inference: Stein variational gradient descent (SVGD) and black-box variational inference (BBVI). In particular, we show that BBVI corresponds precisely to SVGD when the kernel is the neural tangent kernel. Furthermore, we interpret SVGD and BBVI as kernel gradient flows; we do this by leveraging the recent perspective that views SVGD as a gradient flow in the space of probability distributions and showing that BBVI naturally motivates a Riemannian structure on that space. We observe that kernel gradient flow also describes dynamics found in the training of generative adversarial networks (GANs). This work thereby unifies several existing techniques in variational inference and generative modeling and identifies the kernel as a fundamental object governing the behavior of these algorithms, motivating deeper analysis of its properties. | The equivalence between Stein variational gradient descent and black-box variational inference |
d239016426 | Figure 1: One-shot domain adaptation: (left) a single reference image from domain B is used to refine a GAN G A to learn G B ; (center) every image in domain A has an analog in domain B that shares a latent code and many salient attributes; (right) because salient attributes are preserved in the new domain, many latent-edits are meaningful in the new domain.ABSTRACTWe present a new method for one shot domain adaptation. The input to our method is a trained GAN that can produce images in domain A and a single reference image I B from domain B. The proposed algorithm can translate any output of the trained GAN from domain A to domain B. There are two main advantages of our method compared to the current state of the art: First, our solution achieves higher visual quality, e.g. by noticeably reducing overfitting. Second, our solution allows for more degrees of freedom to control the domain gap, i.e. what aspects of the image I B are used to define the domain B. Technically, we realize the new method by building on a pre-trained StyleGAN generator as GAN and a pre-trained CLIP model for representing the domain gap. We propose several new regularizers for controlling the domain gap to optimize the weights of the pre-trained StyleGAN generator so that it will output images in domain B instead of domain A. The regularizers prevent the optimization from taking on too many attributes of the single reference image. Our results show significant visual improvements over the state of the art as well as multiple applications that highlight improved control. | MIND THE GAP: DOMAIN GAP CONTROL FOR SINGLE SHOT DOMAIN ADAPTATION FOR GENERATIVE AD- VERSARIAL NETWORKS |
d248965094 | Calibration is defined as the ratio of the average predicted click rate to the true click rate. The optimization of calibration is essential to many online advertising recommendation systems because it directly affects the downstream bids in ads auctions and the amount of money charged to advertisers. Despite its importance, calibration often suffers from a problem called "maximization bias". Maximization bias refers to the phenomenon that the maximum of predicted values overestimates the true maximum. The problem is introduced because the calibration is computed on the set selected by the prediction model itself. It persists even if unbiased predictions are achieved on every datapoint and worsens when covariate shifts exist between the training and test sets. To mitigate this problem, we quantify maximization bias and propose a variance-adjusting debiasing (VAD) meta-algorithm in this paper. The algorithm is efficient, robust, and practical as it is able to mitigate maximization bias problem under covariate shifts, without incurring additional online serving costs or compromising the ranking performance. We demonstrate the effectiveness of the proposed algorithm using a state-of-the-art recommendation neural network model on a large-scale real-world dataset. | Published as a conference paper at ICLR 2023 CALIBRATION MATTERS: TACKLING MAXIMIZATION BIAS IN LARGE-SCALE ADVERTISING RECOMMENDA- TION SYSTEMS |
d259298566 | Noisy labels can significantly affect the performance of deep neural networks (DNNs). In medical image segmentation tasks, annotations are error-prone due to the high demand in annotation time and in the annotators' expertise. Existing methods mostly assume noisy labels in different pixels are i.i.d. However, segmentation label noise usually has strong spatial correlation and has prominent bias in distribution. In this paper, we propose a novel Markov model for segmentation noisy annotations that encodes both spatial correlation and bias. Further, to mitigate such label noise, we propose a label correction method to recover true label progressively. We provide theoretical guarantees of the correctness of the proposed method. Experiments show that our approach outperforms current stateof-the-art methods on both synthetic and real-world noisy annotations. 1 of a digital image database for chest radiographs with and without a lung nodule. American . 3d u-net based brain tumor segmentation and survival days prediction. In MICCAI Brainlesion Workshop. Springer, 2019a. . Robust medical image segmentation from non-expert annotations with tri-network. In MICCAI, 2020c. | Published as a conference paper at ICLR 2023 LEARNING TO SEGMENT FROM NOISY ANNOTATIONS: A SPATIAL CORRECTION APPROACH |
d256459523 | We propose a method for learning topology-preserving data representations (dimensionality reduction). The method aims to provide topological similarity between the data manifold and its latent representation via enforcing the similarity in topological features (clusters, loops, 2D voids, etc.) and their localization. The core of the method is the minimization of the Representation Topology Divergence (RTD) between original high-dimensional data and low-dimensional representation in latent space. RTD minimization provides closeness in topological features with strong theoretical guarantees. We develop a scheme for RTD differentiation and apply it as a loss term for the autoencoder. The proposed method "RTD-AE" better preserves the global structure and topology of the data manifold than state-of-theart competitors as measured by linear correlation, triplet distance ranking accuracy, and Wasserstein distance between persistence barcodes. * Equal senior contribution. Correspondence: i-tr@yandex.ru | LEARNING TOPOLOGY-PRESERVING DATA REPRESEN- TATIONS |
d209516262 | Sequential word order is important when processing text. Currently, neural networks (NNs) address this by modeling word position using position embeddings. The problem is that position embeddings capture the position of individual words, but not the ordered relationship (e.g., adjacency or precedence) between individual word positions. We present a novel and principled solution for modeling both the global absolute positions of words and their order relationships. Our solution generalizes word embeddings, previously defined as independent vectors, to continuous word functions over a variable (position). The benefit of continuous functions over variable positions is that word representations shift smoothly with increasing positions. Hence, word representations in different positions can correlate with each other in a continuous function. The general solution of these functions is extended to complex-valued domain due to richer representations. We extend CNN, RNN and Transformer NNs to complex-valued versions to incorporate our complex embedding (we make all code available). Experiments on text classification, machine translation and language modeling show gains over both classical word embeddings and position-enriched word embeddings. To our knowledge, this is the first work in NLP to link imaginary numbers in complex-valued representations to concrete meanings (i.e., word order). | Published as a conference paper at ICLR 2020 ENCODING WORD ORDER IN COMPLEX EMBEDDINGS |
d257038893 | Although much of the success of Deep Learning builds on learning good representations, a rigorous method to evaluate their quality is lacking. In this paper, we treat the evaluation of representations as a model selection problem and propose to use the Minimum Description Length (MDL) principle to devise an evaluation metric. Contrary to the established practice of limiting the capacity of the readout model, we design a hybrid discrete and continuous-valued model space for the readout models and employ a switching strategy to combine their predictions. The MDL score takes model complexity, as well as data efficiency into account. As a result, the most appropriate model for the specific task and representation will be chosen, making it a unified measure for comparison. The proposed metric can be efficiently computed with an online method and we present results for pre-trained vision encoders of various architectures (ResNet and ViT) and objective functions (supervised and self-supervised) on a range of downstream tasks. We compare our methods with accuracy-based approaches and show that the latter are inconsistent when multiple readout models are used. Finally, we discuss important properties revealed by our evaluations such as model scaling, preferred readout model, and data efficiency.Published as a conference paper at ICLR 2023 Our contributions are as follows:1. We propose readout model switching for evaluating representations.BACKGROUNDMinimum Description Length is based on the fundamental idea that learning and comprehension correspond to compression(Rathmanner & Hutter, 2011). Given data D=(y t ) N 1 ∈ Y N and a hypothesis space M = {M 1 , M 2 , . . . }, where each hypothesis M corresponds to a parametric probabilistic model p(D|θ, M ), MDL aims to identify the model that can compress the data D best. Considering the close relationship between lossless coding and probability distributions, this can be achieved by associating a codelength function L(D|M )= − log p(D|M ) with each hypothesis. A vast body of literature shows that models with a shorter description length have a better chance of generalizing to future data(Wallace, 2005;Grünwald, 2007a;Rathmanner & Hutter, 2011).A crude way to obtain description lengths is to consider L(D | M )=L M (θ)+L M (D | θ), where L M (θ) is the cost of encoding the parameters and L M (D|θ)=− log p(D|θ, M ) is the cost of compressing the data with the parameterized model. This two-part code approach is intuitive but suboptimal and ambiguous because it does not specify how to encode the parameters. This crude MDL approach has been refined in three distinct but closely related ways: | Published as a conference paper at ICLR 2023 EVALUATING REPRESENTATIONS WITH READOUT MODEL SWITCHING |
d12998557 | Most modern convolutional neural networks (CNNs) used for object recognition are built using the same principles: Alternating convolution and max-pooling layers followed by a small number of fully connected layers. We re-evaluate the state of the art for object recognition from small images with convolutional networks, questioning the necessity of different components in the pipeline. We find that max-pooling can simply be replaced by a convolutional layer with increased stride without loss in accuracy on several image recognition benchmarks. Following this finding -and building on other recent work for finding simple network structures -we propose a new architecture that consists solely of convolutional layers and yields competitive or state of the art performance on several object recognition datasets (CIFAR-10, CIFAR-100, ImageNet). To analyze the network we introduce a new variant of the "deconvolution approach" for visualizing features learned by CNNs, which can be applied to a broader range of network structures than existing approaches. * Both authors contributed equally to this work. | Under review as a conference paper at ICLR 2015 STRIVING FOR SIMPLICITY: THE ALL CONVOLUTIONAL NET |
d252918484 | Deep Ensembles (DE) are a prominent approach for achieving excellent performance on key metrics such as accuracy, calibration, uncertainty estimation, and out-of-distribution detection. However, hardware limitations of real-world systems constrain to smaller ensembles and lower-capacity networks, significantly deteriorating their performance and properties. We introduce Packed-Ensembles (PE), a strategy to design and train lightweight structured ensembles by carefully modulating the dimension of their encoding space. We leverage grouped convolutions to parallelize the ensemble into a single shared backbone and forward pass to improve training and inference speeds. PE is designed to operate within the memory limits of a standard neural network. Our extensive research indicates that PE accurately preserves the properties of DE, such as diversity, and performs equally well in terms of accuracy, calibration, out-of-distribution detection, and robustness to distribution shift. We make our code available at github.com/ENSTA-U2IS/torch-uncertainty. | PACKED-ENSEMBLES FOR EFFICIENT UNCERTAINTY ESTIMATION |
d247447572 | We present a general convergent class of reinforcement learning algorithms that is founded on two distinct principles: (1) mapping value estimates to a different space using arbitrary functions from a broad class, and (2) linearly decomposing the reward signal into multiple channels. The first principle enables incorporating specific properties into the value estimator that can enhance learning. The second principle, on the other hand, allows for the value function to be represented as a composition of multiple utility functions. This can be leveraged for various purposes, e.g. dealing with highly varying reward scales, incorporating a priori knowledge about the sources of reward, and ensemble learning. Combining the two principles yields a general blueprint for instantiating convergent algorithms by orchestrating diverse mapping functions over multiple reward channels. This blueprint generalizes and subsumes algorithms such as Q-Learning, Log Q-Learning, and Q-Decomposition. In addition, our convergence proof for this general class relaxes certain required assumptions in some of these algorithms. Based on our theory, we discuss several interesting configurations as special cases. Finally, to illustrate the potential of the design space that our theory opens up, we instantiate a particular algorithm and evaluate its performance on the Atari suite.1 Action gap refers to the value difference between optimal and second best actions (Farahmand, 2011).One loosely related topic is that of nonlinear Bellman equations. In the canonical formulation of Bellman equations(Bellman, 1954;1957), they are limited in their modeling power to cumulative rewards that are discounted exponentially. However, one may go beyond this basis and redefine the Bellman equations in a general nonlinear manner. In particular,van Hasselt et al. (2019)showed that many such Bellman operators are still contraction mappings and thus the resulting algorithms are reasonable and inherit many beneficial properties of their linear counterparts. Nevertheless, the application of such algorithms is still unclear since the fixed point does not have a direct connection to the concept of return. In this paper we do not consider nonlinear Bellman equations.Continuing with the first line of thought, a natural extension is to employ multiple mapping functions concurrently in an ensemble, allowing each to contribute their own benefits. This can be viewed as a form of separation of concerns(van Seijen et al., 2016). Ideally, we may want to dynamically modify the influence of different mappings as the learning advances. For example, the agent could start with mappings that facilitate learning on sparse rewards. Then, as it learns to collect more rewards, the mapping function can be gradually adapted to better support learning on denser rewards. Moreover, there may be several sources of reward with specific characteristics (e.g. sparse positive rewards but dense negative ones), in which case using a different mapping to deal with each reward channel could prove beneficial.Building upon these ideas, this paper presents a general class of algorithms based on the combination of two distinct principles: value mapping and linear reward decomposition. Specifically, we present a broad class of mapping functions that inherit the convergence properties of the basis algorithm. We further show that such mappings can be orchestrated through linear reward decomposition, proving convergence for the complete class of resulting algorithms. The outcome is a blueprint for building new convergent algorithms as instances. We conceptually discuss several interesting configurations, and experimentally validate one particular instance on the Atari 2600 suite. | Published as a conference paper at ICLR 2022 ORCHESTRATED VALUE MAPPING FOR REINFORCEMENT LEARNING |
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