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d238743865 | Understanding the implicit bias of Stochastic Gradient Descent (SGD) is one of the key challenges in deep learning, especially for overparametrized models, where the local minimizers of the loss function L can form a manifold. Intuitively, with a sufficiently small learning rate η, SGD tracks Gradient Descent (GD) unti... | What Happens after SGD Reaches Zero Loss? -A Mathematical Framework |
d227210816 | Formal verification of neural networks (NNs) is a challenging and important problem. Existing efficient complete solvers typically require the branch-and-bound (BaB) process, which splits the problem domain into sub-domains and solves each sub-domain using faster but weaker incomplete verifiers, such as Linear Programm... | Fast and Complete: Enabling Complete Neural Network Veri- fication with Rapid and Massively Parallel Incomplete Verifiers |
d213896662 | We address the challenging problem of deep representation learning -the efficient adaption of a pre-trained deep network to different tasks. Specifically, we propose to explore gradient-based features. These features are gradients of the model parameters with respect to a task-specific loss given an input sample. Our k... | Published as a conference paper at ICLR 2020 GRADIENTS AS FEATURES FOR DEEP REPRESENTATION LEARNING |
d238215749 | Training multi-layer Graph Convolution Networks (GCN) using standard SGD techniques scales poorly as each descent step ends up updating node embeddings for a large portion of the graph. Recent attempts to remedy this sub-sample the graph that reduce compute but introduce additional variance and may offer suboptimal per... | IGLU: EFFICIENT GCN TRAINING VIA LAZY UP- DATES |
d238744008 | Emphatic temporal difference (ETD) learning [26] is a successful method to conduct the off-policy value function evaluation with function approximation. Although ETD has been shown to converge asymptotically to a desirable value function, it is well-known that ETD often encounters a large variance so that its sample c... | PER-ETD: A Polynomially Efficient Emphatic Temporal Difference Learning Method * |
d8535316 | Machine comprehension (MC), answering a query about a given context paragraph, requires modeling complex interactions between the context and the query. Recently, attention mechanisms have been successfully extended to MC. Typically these methods use attention to focus on a small portion of the context and summarize it... | BI-DIRECTIONAL ATTENTION FLOW FOR MACHINE COMPREHENSION |
d247411310 | Offline reinforcement learning, which seeks to utilize offline/historical data to optimize sequential decision-making strategies, has gained surging prominence in recent studies. Due to the advantage that appropriate function approximators can help mitigate the sample complexity burden in modern reinforcement learning ... | Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism * |
d3579956 | Deep neural networks have excelled on a wide range of problems, from vision to language and game playing. Neural networks very gradually incorporate information into weights as they process data, requiring very low learning rates. If the training distribution shifts, the network is slow to adapt, and when it does adapt... | Published as a conference paper at ICLR 2018 MEMORY-BASED PARAMETER ADAPTATION |
d250072949 | While diffusion models have shown great success in image generation, their noise-inverting generative process does not explicitly consider the structure of images, such as their inherent multi-scale nature. Inspired by diffusion models and the empirical success of coarse-to-fine modelling, we propose a new diffusion-li... | Published as a conference paper at ICLR 2023 GENERATIVE MODELLING WITH INVERSE HEAT DISSIPATION |
d253761231 | As data becomes increasingly vital, a company would be very cautious about releasing data, because the competitors could use it to train high-performance models, thereby posing a tremendous threat to the company's commercial competence. To prevent training good models on the data, we could add imperceptible perturbatio... | SELF-ENSEMBLE PROTECTION: TRAINING CHECK- POINTS ARE GOOD DATA PROTECTORS |
d231627538 | We propose greedy and local search algorithms for rank-constrained convex optimization, namely solving min rank(A)≤r * R(A) given a convex function R : R m×n → R and a parameter r * . These algorithms consist of repeating two steps: (a) adding a new rank-1 matrix to A and (b) enforcing the rank constraint on A. We refi... | Published as a conference paper at ICLR 2021 LOCAL SEARCH ALGORITHMS FOR RANK- CONSTRAINED CONVEX OPTIMIZATION |
d203953161 | Manifold assumption in learning states that: the data lie approximately on a manifold of much lower dimension than the input space. Generative models learn to generate data according to the underlying data distribution. Generative models are used in various tasks, such as data augmentation and generating variation of i... | ON NEED FOR TOPOLOGY AWARENESS OF GENERA- TIVE MODELS |
d17127188 | State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification. However, dense prediction problems such as semantic segmentation are structurally different from image classification. In this work, we develop a new convoluti... | MULTI-SCALE CONTEXT AGGREGATION BY DILATED CONVOLUTIONS |
d256274785 | The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization performance. In this paper, we first revisit the popular pseudo-labeling methods via a unified sample weighting formulation and demonstrate th... | SOFTMATCH: ADDRESSING THE QUANTITY-QUALITY TRADE-OFF IN SEMI-SUPERVISED LEARNING |
d252683604 | Graph contrastive learning (GCL), as an emerging self-supervised learning technique on graphs, aims to learn representations via instance discrimination. Its performance heavily relies on graph augmentation to reflect invariant patterns that are robust to small perturbations; yet it still remains unclear about what gra... | Published as a conference paper at ICLR 2023 SPECTRAL AUGMENTATION FOR SELF-SUPERVISED LEARNING ON GRAPHS |
d235683116 | We consider the problem of object goal navigation in unseen environments. Solving this problem requires learning of contextual semantic priors, a challenging endeavour given the spatial and semantic variability of indoor environments. Current methods learn to implicitly encode these priors through goal-oriented navigat... | Published as a conference paper at ICLR 2022 LEARNING TO MAP FOR ACTIVE SEMANTIC GOAL NAVIGATION |
d252918693 | An oft-cited challenge of federated learning is the presence of heterogeneity. Data heterogeneity refers to the fact that data from different clients may follow very different distributions. System heterogeneity refers to the fact that client devices have different system capabilities. A considerable number of federate... | WHERE TO BEGIN? ON THE IMPACT OF PRE- TRAINING AND INITIALIZATION IN FEDERATED LEARNING |
d222341528 | Recent approaches to efficiently ensemble neural networks have shown that strong robustness and uncertainty performance can be achieved with a negligible gain in parameters over the original network. However, these methods still require multiple forward passes for prediction, leading to a significant computational cost... | Published as a conference paper at ICLR 2021 TRAINING INDEPENDENT SUBNETWORKS FOR ROBUST PREDICTION |
d210023826 | Temporal difference (TD) learning is a popular algorithm for policy evaluation in reinforcement learning, but the vanilla TD can substantially suffer from the inherent optimization variance. A variance reduced TD (VRTD) algorithm was proposed by Korda and La (2015), which applies the variance reduction technique direct... | REANALYSIS OF VARIANCE REDUCED TEMPORAL DIF- FERENCE LEARNING |
d2158905 | When analyzing the genome, researchers have discovered that proteins bind to DNA based on certain patterns on the DNA sequence known as "motifs". However, it is difficult to manually construct motifs due to their complexity. Recently, external learned memory models have proven to be effective methods for reasoning over... | Workshop track -ICLR 2017 MEMORY MATCHING NETWORKS FOR GENOMIC SEQUENCE CLASSIFICATION |
d219981345 | Post-hoc multi-class calibration is a common approach for providing high-quality confidence estimates of deep neural network predictions. Recent work has shown that widely used scaling methods underestimate their calibration error, while alternative Histogram Binning (HB) methods often fail to preserve classification a... | MULTI-CLASS UNCERTAINTY CALIBRATION VIA MU- TUAL INFORMATION MAXIMIZATION-BASED BINNING |
d11280172 | The efficiency of graph-based semi-supervised algorithms depends on the graph of instances on which they are applied. The instances are often in a vectorial form before a graph linking them is built. The construction of the graph relies on a metric over the vectorial space that help define the weight of the connection ... | A METRIC LEARNING APPROACH FOR GRAPH-BASED LABEL PROPAGATION |
d257255156 | Privacy and security concerns in real-world applications have led to the development of adversarially robust federated models. However, the straightforward combination between adversarial training and federated learning in one framework can lead to the undesired robustness deterioration. We discover that the attributio... | Published as a conference paper at ICLR 2023 COMBATING EXACERBATED HETEROGENEITY FOR ROBUST MODELS IN FEDERATED LEARNING |
d249712412 | End-to-end learning for visual robotic manipulation is known to suffer from sample inefficiency, requiring large numbers of demonstrations. The spatial rototranslation equivariance, or the SE(3)-equivariance can be exploited to improve the sample efficiency for learning robotic manipulation. In this paper, we present S... | Published as a conference paper at ICLR 2023 EQUIVARIANT DESCRIPTOR FIELDS: SE(3)- EQUIVARIANT ENERGY-BASED MODELS FOR END- TO-END VISUAL ROBOTIC MANIPULATION LEARNING |
d248693515 | This work considers identifying parameters characterizing a physical system's dynamic motion directly from a video whose rendering configurations are inaccessible. Existing solutions require massive training data or lack generalizability to unknown rendering configurations. We propose a novel approach that marries doma... | Published as a conference paper at ICLR 2022 RISP: RENDERING-INVARIANT STATE PREDICTOR WITH DIFFERENTIABLE SIMULATION AND RENDER- ING FOR CROSS-DOMAIN PARAMETER ESTIMATION |
d231609191 | With increasingly more data and computation involved in their training, machine learning models constitute valuable intellectual property. This has spurred interest in model stealing, which is made more practical by advances in learning with partial, little, or no supervision. Existing defenses focus on inserting uniqu... | Published as a conference paper at ICLR 2021 DATASET INFERENCE: OWNERSHIP RESOLUTION IN MACHINE LEARNING |
d15615409 | When deep learning is applied to visual object recognition, data augmentation is often used to generate additional training data without extra labeling cost. It helps to reduce overfitting and increase the performance of the algorithm. In this paper we investigate if it is possible to use data augmentation as the main ... | Unsupervised feature learning by augmenting single images |
d30472538 | We introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all within a realistic context. HoME integrates over 45,000 diverse 3D house layouts based on the SUNCG dataset, a scale which may facilitate learni... | HoME: a Household Multimodal Environment |
d219721506 | Checkpointing enables training larger models by freeing intermediate activations and recomputing them on demand. Previous checkpointing techniques are difficult to generalize to dynamic models because they statically plan recomputations offline. We present Dynamic Tensor Rematerialization (DTR), a greedy online algorit... | DYNAMIC TENSOR REMATERIALIZATION A PREPRINT |
d209832429 | In order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones. However, a critical challenge in meta-learning is the task heterogeneity which cannot be well handled by traditional globally shared meta-learning methods. In addition, cu... | Published as a conference paper at ICLR 2020 AUTOMATED RELATIONAL META-LEARNING |
d238583108 | To mitigate the burden of data labeling, we aim at improving data efficiency for both classification and regression setups in deep learning. However, the current focus is on classification problems while rare attention has been paid to deep regression, which usually requires more human effort to labeling. Further, due ... | Preliminary work χ-MODEL: IMPROVING DATA EFFICIENCY IN DEEP LEARNING WITH A MINIMAX MODEL |
d59608662 | The loss function of deep networks is known to be non-convex but the precise nature of this nonconvexity is still an active area of research. In this work, we study the loss landscape of deep networks through the eigendecompositions of their Hessian matrix. In particular, we examine how important the negative eigenvalu... | Negative eigenvalues of the Hessian in deep neural networks |
d252596129 | Effective data-driven PDE forecasting methods often rely on fixed spatial and / or temporal discretizations. This raises limitations in real-world applications like weather prediction where flexible extrapolation at arbitrary spatiotemporal locations is required. We address this problem by introducing a new data-driven... | Published as a conference paper at ICLR 2023 CONTINUOUS PDE DYNAMICS FORECASTING WITH IMPLICIT NEURAL REPRESENTATIONS |
d1528564 | Deep neural networks are powerful parametric models that can be trained efficiently using the backpropagation algorithm. Stochastic neural networks combine the power of large parametric functions with that of graphical models, which makes it possible to learn very complex distributions. However, as backpropagation is n... | MUPROP: UNBIASED BACKPROPAGATION FOR STOCHASTIC NEURAL NETWORKS |
d239016143 | Q-learning is a popular Reinforcement Learning (RL) algorithm which is widely used in practice with function approximation (Mnih et al., 2015). In contrast, existing theoretical results are pessimistic about Q-learning. For example, (Baird, 1995) shows that Q-learning does not converge even with linear function appro... | ONLINE TARGET Q-LEARNING WITH REVERSE EXPE- RIENCE REPLAY: EFFICIENTLY FINDING THE OPTIMAL POLICY FOR LINEAR MDPS |
d239009856 | Many text generation systems benefit from using a retriever to retrieve passages from a textual knowledge corpus (e.g., Wikipedia) and providing these passages as additional context to the generator. For open-ended generation tasks (like generating informative utterances in conversations) many varied passages may be eq... | HINDSIGHT: POSTERIOR-GUIDED TRAINING OF RE- TRIEVERS FOR IMPROVED OPEN-ENDED GENERATION |
d23884510 | Stacked denoising auto encoders (DAEs) are well known to learn useful deep representations, which can be used to improve supervised training by initializing a deep network. We investigate a training scheme of a deep DAE, where DAE layers are gradually added and keep adapting as additional layers are added. We show that... | GRADUAL TRAINING METHOD FOR DENOISING AUTO ENCODERS |
d15734062 | We provide novel guaranteed approaches for training feedforward neural networks with sparse connectivity. We leverage on the techniques developed previously for learning linear networks and show that they can also be effectively adopted to learn non-linear networks. We operate on the moments involving label and the sco... | PROVABLE METHODS FOR TRAINING NEURAL NET- WORKS WITH SPARSE CONNECTIVITY |
d257378676 | In this paper, we propose a new self-supervised method, which is called Denoising Masked AutoEncoders (DMAE), for learning certified robust classifiers of images. In DMAE, we corrupt each image by adding Gaussian noises to each pixel value and randomly masking several patches. A Transformer-based encoder-decoder model ... | Published as a conference paper at ICLR 2023 DENOISING MASKED AUTOENCODERS HELP ROBUST CLASSIFICATION |
d253224276 | Generating complex behaviors that satisfy the preferences of non-expert users is a crucial requirement for AI agents. Interactive reward learning from trajectory comparisons (a.k.a. RLHF) is one way to allow non-expert users to convey complex objectives by expressing preferences over short clips of agent behaviors. Eve... | Published as a conference paper at ICLR 2023 RELATIVE BEHAVIORAL ATTRIBUTES: FILLING THE GAP BETWEEN SYMBOLIC GOAL SPECIFICATION AND REWARD LEARNING FROM HUMAN PREFERENCES |
d258557844 | Beyond the success story of adversarial training (AT) in the recent text domain on top of pre-trained language models (PLMs), our empirical study showcases the inconsistent gains from AT on some tasks, e.g. commonsense reasoning, named entity recognition. This paper investigates AT from the perspective of the contextua... | Published as a conference paper at ICLR 2023 TOWARD ADVERSARIAL TRAINING ON CONTEXTUAL- IZED LANGUAGE REPRESENTATION |
d252683116 | In representation learning, a common approach is to seek representations which disentangle the underlying factors of variation. Eastwood & Williams (2018) proposed three metrics for quantifying the quality of such disentangled representations: disentanglement (D), completeness (C) and informativeness (I).In this work, ... | DCI-ES: AN EXTENDED DISENTANGLEMENT FRAME- WORK WITH CONNECTIONS TO IDENTIFIABILITY |
d247363675 | Equivariant neural networks enforce symmetry within the structure of their convolutional layers, resulting in a substantial improvement in sample efficiency when learning an equivariant or invariant function. Such models are applicable to robotic manipulation learning which can often be formulated as a rotationally sym... | SO(2)-EQUIVARIANT REINFORCEMENT LEARNING |
d52937611 | Recurrent neural networks are known for their notorious exploding and vanishing gradient problem (EVGP). This problem becomes more evident in tasks where the information needed to correctly solve them exist over long time scales, because EVGP prevents important gradient components from being back-propagated adequately ... | h-DETACH: MODIFYING THE LSTM GRADIENT TO- WARDS BETTER OPTIMIZATION |
d214667180 | Image captioning models have been able to generate grammatically correct and human understandable sentences. However most of the captions convey limited information as the model used is trained on datasets that do not caption all possible objects existing in everyday life. Due to this lack of prior information most of ... | Egoshots, AN EGO-VISION LIFE-LOGGING DATASET AND SEMANTIC FIDELITY METRIC TO EVALUATE DIVERSITY IN IMAGE CAPTIONING MODELS |
d220968818 | We show how to assess a language model's knowledge of basic concepts of morality. We introduce the ETHICS dataset, a new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. Models predict widespread moral judgments about diverse text scenarios. This requires connecting physi... | Published as a conference paper at ICLR 2021 ALIGNING AI WITH SHARED HUMAN VALUES |
d651286 | We investigate multiple techniques to improve upon the current state of the art deep convolutional neural network based image classification pipeline. The techniques include adding more image transformations to the training data, adding more transformations to generate additional predictions at test time and using comp... | Some Improvements on Deep Convolutional Neural Network Based Image Classification |
d257254973 | Learning to Optimize (L2O) has drawn increasing attention as it often remarkably accelerates the optimization procedure of complex tasks by "overfitting" specific task types, leading to enhanced performance compared to analytical optimizers. Generally, L2O develops a parameterized optimization method (i.e., "optimizer"... | M-L2O: TOWARDS GENERALIZABLE LEARNING-TO- OPTIMIZE BY TEST-TIME FAST SELF-ADAPTATION |
d239050183 | Traffic forecasting is a challenging problem due to complex road networks and sudden speed changes caused by various events on roads. Several models have been proposed to solve this challenging problem, with a focus on learning the spatio-temporal dependencies of roads. In this work, we propose a new perspective for co... | LEARNING TO REMEMBER PATTERNS: PATTERN MATCHING MEMORY NETWORKS FOR TRAFFIC FORE- CASTING |
d252595953 | Learning mappings between infinite dimensional function spaces has achieved empirical success in many disciplines of machine learning, including generative modeling, machine learning solving Partial Difference Equations , functional data analysis, causal inference, and multi-agent reinforcement learning. In this paper,... | Working Paper. Do not distribute |
d209202200 | Answering compositional questions that require multiple steps of reasoning against text is challenging, especially when they involve discrete, symbolic operations. Neural module networks (NMNs) learn to parse such questions as executable programs composed of learnable modules, performing well on synthetic visual QA dom... | Published as a conference paper at ICLR 2020 NEURAL MODULE NETWORKS FOR REASONING OVER TEXT |
d222133021 | In attempts to produce machine learning models less reliant on spurious patterns in training data, researchers have recently proposed a human-in-the-loop process for generating counterfactually augmented datasets. As applied in NLP, given some documents and their (initial) labels, humans are tasked with revising the te... | Explaining The Efficacy of Counterfactually-Augmented Data |
d3693512 | We propose a novel deep network architecture for lifelong learning which we refer to as Dynamically Expandable Network (DEN), that can dynamically decide its network capacity as it trains on a sequence of tasks, to learn a compact overlapping knowledge sharing structure among tasks. DEN is efficiently trained in an onl... | LIFELONG LEARNING WITH DYNAMICALLY EXPAND- ABLE NETWORKS |
d246823055 | Continual learning needs to overcome catastrophic forgetting of the past. Memory replay of representative old training samples has been shown as an effective solution, and achieves the state-of-the-art (SOTA) performance. However, existing work is mainly built on a small memory buffer containing a few original data, wh... | Published as a conference paper at ICLR 2022 MEMORY REPLAY WITH DATA COMPRESSION FOR CONTINUAL LEARNING |
d256459559 | Cutting planes (cuts) are important for solving mixed-integer linear programs (MILPs), which formulate a wide range of important real-world applications. Cut selection-which aims to select a proper subset of the candidate cuts to improve the efficiency of solving MILPs-heavily depends on (P1) which cuts should be prefe... | Published as a conference paper at ICLR 2023 LEARNING CUT SELECTION FOR MIXED-INTEGER LINEAR PROGRAMMING VIA HIERARCHICAL SEQUENCE MODEL |
d213659918 | Previous studies have found that an adversary attacker can often infer unintended input information from intermediate-layer features. We study the possibility of preventing such adversarial inference, yet without too much accuracy degradation. We propose a generic method to revise the neural network to boost the challe... | INTERPRETABLE COMPLEX-VALUED NEURAL NET- WORKS FOR PRIVACY PROTECTION |
d203610572 | We empirically evaluate common assumptions about neural networks that are widely held by practitioners and theorists alike. In this work, we: (1) prove the widespread existence of suboptimal local minima in the loss landscape of neural networks, and we use our theory to find examples; (2) show that small-norm parameter... | TRUTH OR BACKPROPAGANDA? AN EMPIRICAL IN- VESTIGATION OF DEEP LEARNING THEORY |
d251066662 | The problem of how to genetically modify cells in order to maximize a certain cellular phenotype has taken center stage in drug development over the last few years (with, for example, genetically edited CAR-T, CAR-NK, and CAR-NKT cells entering cancer clinical trials). Exhausting the search space for all possible genet... | Published as a conference paper at ICLR 2023 NEURAL DESIGN FOR GENETIC PERTURBATION EX- PERIMENTS |
d214495004 | We are interested in derivative-free optimization of high-dimensional functions. The sample complexity of existing methods is high and depends on problem dimensionality, unlike the dimensionality-independent rates of first-order methods. The recent success of deep learning suggests that many datasets lie on low-dimensi... | Published as a conference paper at ICLR 2020 LEARNING TO GUIDE RANDOM SEARCH |
d248572465 | Based on large-scale pre-trained multilingual representations, recent cross-lingual transfer methods have achieved impressive transfer performances. However, the performance of target languages still lags far behind the source language. In this paper, our analyses indicate such a performance gap is strongly associated ... | ENHANCING CROSS-LINGUAL TRANSFER BY MANI- FOLD MIXUP |
d18166574 | A key challenge in designing convolutional network models is sizing them appropriately. Many factors are involved in these decisions, including number of layers, feature maps, kernel sizes, etc. Complicating this further is the fact that each of these influence not only the numbers and dimensions of the activation unit... | Understanding Deep Architectures using a Recursive Convolutional Network |
d3525710 | Convolutional Neural Networks (CNNs) have become the method of choice for learning problems involving 2D planar images. However, a number of problems of recent interest have created a demand for models that can analyze spherical images. Examples include omnidirectional vision for drones, robots, and autonomous cars, mo... | Published as a conference paper at ICLR 2018 SPHERICAL CNNS |
d256827775 | We propose a conditional positional encoding (CPE) scheme for vision Transformers (Dosovitskiy et al., 2021;Touvron et al., 2020). Unlike previous fixed or learnable positional encodings that are predefined and independent of input tokens, CPE is dynamically generated and conditioned on the local neighborhood of the in... | Published as a conference paper at ICLR 2023 CONDITIONAL POSITIONAL ENCODINGS FOR VISION TRANSFORMERS |
d6820006 | Ensembles of neural networks are known to be much more robust and accurate than individual networks. However, training multiple deep networks for model averaging is computationally expensive. In this paper, we propose a method to obtain the seemingly contradictory goal of ensembling multiple neural networks at no addit... | Published as a conference paper at ICLR 2017 SNAPSHOT ENSEMBLES: TRAIN 1, GET M FOR FREE |
d59336240 | We explore various methods for computing sentence representations from pretrained word embeddings without any training, i.e., using nothing but random parameterizations. Our aim is to put sentence embeddings on more solid footing by 1) looking at how much modern sentence embeddings gain over random methods-as it turns ... | NO TRAINING REQUIRED: EXPLORING RANDOM EN- CODERS FOR SENTENCE CLASSIFICATION |
d10872458 | Training deep directed graphical models with many hidden variables and performing inference remains a major challenge. Helmholtz machines and deep belief networks are such models, and the wake-sleep algorithm has been proposed to train them. The wake-sleep algorithm relies on training not just the directed generative m... | Reweighted Wake-Sleep |
d219687094 | The properties of flat minima in the empirical risk landscape of neural networks have been debated for some time. Increasing evidence suggests they possess better generalization capabilities with respect to sharp ones. In this work we first discuss the relationship between alternative measures of flatness: The local en... | ENTROPIC GRADIENT DESCENT ALGORITHMS AND WIDE FLAT MINIMA A PREPRINT |
d253237810 | Empirical studies of the loss landscape of deep networks have revealed that many local minima are connected through low-loss valleys. Yet, little is known about the theoretical origin of such valleys. We present a general framework for finding continuous symmetries in the parameter space, which carve out low-loss valle... | Published as a conference paper at ICLR 2023 SYMMETRIES, FLAT MINIMA, AND THE CONSERVED QUANTITIES OF GRADIENT FLOW |
d4842909 | Current end-to-end machine reading and question answering (Q&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for both training and inference due to the sequential nature of RNNs. We propose a new Q&A architecture called QANet, which do... | Published as a conference paper at ICLR 2018 QANET: COMBINING LOCAL CONVOLUTION WITH GLOBAL SELF-ATTENTION FOR READING COMPRE- HENSION |
d227342850 | Establishing a theoretical analysis that explains why deep learning can outperform shallow learning such as kernel methods is one of the biggest issues in the deep learning literature. Towards answering this question, we evaluate excess risk of a deep learning estimator trained by a noisy gradient descent with ridge re... | Benefit of deep learning with non-convex noisy gradient descent BENEFIT OF DEEP LEARNING WITH NON-CONVEX NOISY GRADIENT DESCENT: PROVABLE EXCESS RISK BOUND AND SUPERIORITY TO KERNEL METHODS |
d208267807 | Answering questions that require multi-hop reasoning at web-scale necessitates retrieving multiple evidence documents, one of which often has little lexical or semantic relationship to the question. This paper introduces a new graphbased recurrent retrieval approach that learns to retrieve reasoning paths over the Wiki... | Published as a conference paper at ICLR 2020 LEARNING TO RETRIEVE REASONING PATHS OVER WIKIPEDIA GRAPH FOR QUESTION ANSWERING |
d256503461 | Self-supervised learning aims to learn a embedding space where semantically similar samples are close. Contrastive learning methods pull views of samples together and push different samples away, which utilizes semantic invariance of augmentation but ignores the relationship between samples. To better exploit the power... | Published as a conference paper at ICLR 2023 AUGMENTATION COMPONENT ANALYSIS: MODELING SIMILARITY VIA THE AUGMENTATION OVERLAPS |
d53501574 | Image-to-image translation has recently received significant attention due to advances in deep learning. Most works focus on learning either a one-to-one mapping in an unsupervised way or a many-to-many mapping in a supervised way. However, a more practical setting is many-to-many mapping in an unsupervised way, which ... | EXEMPLAR GUIDED UNSUPERVISED IMAGE-TO- IMAGE TRANSLATION WITH SEMANTIC CONSISTENCY |
d209439606 | We make the following striking observation: fully convolutional VAE models trained on 32×32 ImageNet can generalize well, not just to 64×64 but also to far larger photographs, with no changes to the model. We use this property, applying fully convolutional models to lossless compression, demonstrating a method to scale... | HILLOC: LOSSLESS IMAGE COMPRESSION WITH HI- ERARCHICAL LATENT VARIABLE MODELS |
d3570621 | We present two simple ways of reducing the number of parameters and accelerating the training of large Long Short-Term Memory (LSTM) networks: the first one is "matrix factorization by design" of LSTM matrix into the product of two smaller matrices, and the second one is partitioning of LSTM matrix, its inputs and stat... | Workshop track -ICLR 2017 FACTORIZATION TRICKS FOR LSTM NETWORKS |
d238531645 | Sparsely activated models (SAMs), such as Mixture-of-Experts (MoE), can easily scale to have outrageously large amounts of parameters without significant increase in computational cost. However, SAMs are reported to be parameter inefficient such that larger models do not always lead to better performance. While most on... | Published as a conference paper at ICLR 2022 TAMING SPARSELY ACTIVATED TRANSFORMER WITH STOCHASTIC EXPERTS |
d232185279 | Training classifiers under fairness constraints such as group fairness, regularizes the disparities of predictions between the groups. Nevertheless, even though the constraints are satisfied during training, they might not generalize at evaluation time. To improve the generalizability of fair classifiers, we propose fa... | Published as a conference paper at ICLR 2021 FAIR MIXUP: FAIRNESS VIA INTERPOLATION |
d56517468 | Recurrent neural networks (RNNs) can learn continuous vector representations of symbolic structures such as sequences and sentences; these representations often exhibit linear regularities (analogies). Such regularities motivate our hypothesis that RNNs that show such regularities implicitly compile symbolic structures... | RNNS IMPLICITLY IMPLEMENT TENSOR PRODUCT REPRESENTATIONS |
d222134076 | Recent methods for learning unsupervised visual representations, dubbed contrastive learning, optimize the noise-contrastive estimation (NCE) bound on mutual information between two views of an image. NCE uses randomly sampled negative examples to normalize the objective. In this paper, we show that choosing difficult ... | CONDITIONAL NEGATIVE SAMPLING FOR CON- TRASTIVE LEARNING OF VISUAL REPRESENTATIONS |
d222208634 | Flexible neural sequence models outperform grammar-and automaton-based counterparts on a variety of tasks. However, neural models perform poorly in settings requiring compositional generalization beyond the training data-particularly to rare or unseen subsequences. Past work has found symbolic scaffolding (e.g. grammar... | Published as a conference paper at ICLR 2021 LEARNING TO RECOMBINE AND RESAMPLE DATA FOR COMPOSITIONAL GENERALIZATION |
d246441923 | Differentiable physics modeling combines physics models with gradient-based learning to provide model explicability and data efficiency. It has been used to learn dynamics, solve inverse problems and facilitate design, and is at its inception of impact. Current successes have concentrated on general physics models such... | FINE-GRAINED DIFFERENTIABLE PHYSICS: A YARN- LEVEL MODEL FOR FABRICS |
d209439872 | Recent breakthroughs of pretrained language models have shown the effectiveness of self-supervised learning for a wide range of natural language processing (NLP) tasks. In addition to standard syntactic and semantic NLP tasks, pretrained models achieve strong improvements on tasks that involve real-world knowledge, sug... | Published as a conference paper at ICLR 2020 PRETRAINED ENCYCLOPEDIA: WEAKLY SUPERVISED KNOWLEDGE-PRETRAINED LANGUAGE MODEL |
d254535822 | Training large, deep neural networks to convergence can be prohibitively expensive. As a result, often only a small selection of popular, dense models are reused across different contexts and tasks. Increasingly, sparsely activated models, which seek to decouple model size from computation costs, are becoming an attrac... | Published as a conference paper at ICLR 2023 SPARSE UPCYCLING: TRAINING MIXTURE-OF-EXPERTS FROM DENSE CHECKPOINTS |
d15085443 | We evaluate natural gradient, an algorithm originally proposed in Amari (1997), for learning deep models. The contributions of this paper are as follows. We show the connection between natural gradient and three other recently proposed methods: Hessian-Free (Martens, 2010), Krylov Subspace Descent (Vinyals and Povey, 2... | Revisiting natural gradient for deep networks |
d222140788 | Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps in order to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of ite... | Published as a conference paper at ICLR 2021 DENOISING DIFFUSION IMPLICIT MODELS |
d209832510 | Signatory is a library for calculating signature and logsignature transforms and related functionality. The focus is on making this functionality available for use in machine learning, and as such includes features such as GPU support and backpropagation. To our knowledge it is the first publically available GPU-capabl... | SIGNATORY: DIFFERENTIABLE COMPUTATIONS OF THE SIGNATURE AND LOGSIGNATURE TRANSFORMS, ON BOTH CPU AND GPU * |
d232146803 | Dropout has been demonstrated as a simple and effective module to not only regularize the training process of deep neural networks, but also provide the uncertainty estimation for prediction. However, the quality of uncertainty estimation is highly dependent on the dropout probabilities. Most current models use the sam... | |
d245131210 | We are interested in interactive agents that learn to coordinate, namely, a builder -which performs actions but ignores the goal of the task, i.e. has no access to rewards -and an architect which guides the builder towards the goal of the task. We define and explore a formal setting where artificial agents are equipped... | Published as a conference paper at ICLR 2022 LEARNING TO GUIDE AND TO BE GUIDED IN THE ARCHITECT-BUILDER PROBLEM |
d59222819 | We investigate how reinforcement learning agents can learn to cooperate. Drawing inspiration from human societies, in which successful coordination of many individuals is often facilitated by hierarchical organisation, we introduce Feudal Multiagent Hierarchies (FMH). In this framework, a 'manager' agent, which is task... | Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning |
d9556453 | Code super-optimization is the task of transforming any given program to a more efficient version while preserving its input-output behaviour. In some sense, it is similar to the paraphrase problem from natural language processing where the intention is to change the syntax of an utterance without changing its semantic... | Published as a conference paper at ICLR 2017 LEARNING TO SUPEROPTIMIZE PROGRAMS |
d233423379 | Standard dynamics models for continuous control make use of feedforward computation to predict the conditional distribution of next state and reward given current state and action using a multivariate Gaussian with a diagonal covariance structure. This modeling choice assumes that different dimensions of the next state... | Published as a conference paper at ICLR 2021 AUTOREGRESSIVE DYNAMICS MODELS FOR OFFLINE POLICY EVALUATION AND OPTIMIZATION |
d5069968 | Deep neural networks (DNN) have shown unprecedented success in various computer vision applications such as image classification and object detection. However, it is still a common (yet inconvenient) practice to prepare at least tens of thousands of labeled image to finetune a network on every task before the model is ... | Revisiting Batch Normalization For Practical Domain Adaptation |
d257079046 | A fundamental challenge for multi-task learning is that different tasks may conflict with each other when they are solved jointly, and a cause of this phenomenon is conflicting gradients during optimization. Recent works attempt to mitigate the influence of conflicting gradients by directly altering the gradients based... | Published as a conference paper at ICLR 2023 RECON: REDUCING CONFLICTING GRADIENTS FROM THE ROOT FOR MULTI-TASK LEARNING |
d226226888 | Large-scale language models have recently demonstrated impressive empirical performance. Nevertheless, the improved results are attained at the price of bigger models, more power consumption, and slower inference, which hinder their applicability to low-resource (memory and computation) platforms. Knowledge distillatio... | Pre-print MIXKD: TOWARDS EFFICIENT DISTILLATION OF LARGE-SCALE LANGUAGE MODELS |
d247025836 | Audio-visual navigation task requires an agent to find a sound source in a realistic, unmapped 3D environment by utilizing egocentric audio-visual observations. Existing audio-visual navigation works assume a clean environment that solely contains the target sound, which, however, would not be suitable in most realworl... | Published as a conference paper at ICLR 2022 SOUND ADVERSARIAL AUDIO-VISUAL NAVIGATION |
d246430782 | Implicit neural representations with multi-layer perceptrons (MLPs) have recently gained prominence for a wide variety of tasks such as novel view synthesis and 3D object representation and rendering. However, a significant challenge with these representations is that both training and inference with an MLP over a larg... | COORDX: ACCELERATING IMPLICIT NEURAL REPRE- SENTATION WITH A SPLIT MLP ARCHITECTURE |
d246240044 | In image generation, generative models can be evaluated naturally by visually inspecting model outputs. However, this is not always the case for graph generative models (GGMs), making their evaluation challenging. Currently, the standard process for evaluating GGMs suffers from three critical limitations: i) it does no... | Published as a conference paper at ICLR 2022 ON EVALUATION METRICS FOR GRAPH GENERATIVE MODELS |
d250279999 | Hamiltonian Monte Carlo (HMC) is a popular method in sampling. While there are quite a few works of studying this method on various aspects, an interesting question is how to choose its integration time to achieve acceleration. In this work, we consider accelerating the process of sampling from a distribution π(x) ∝ ex... | Published as a conference paper at ICLR 2023 ACCELERATING HAMILTONIAN MONTE CARLO VIA CHEBYSHEV INTEGRATION TIME |
d239616317 | Pruning neural networks reduces inference time and memory costs.On standard hardware, these benefits will be especially prominent if coarse-grained structures, like feature maps, are pruned.We devise two novel saliency-based methods for second-order structured pruning (SOSP) which include correlations among all structu... | SOSP: EFFICIENTLY CAPTURING GLOBAL CORRELA-TIONS BY SECOND-ORDER STRUCTURED PRUNING |
d225066968 | We present a new methodology for detecting out-of-distribution (OOD) images by utilizing norms of the score estimates at multiple noise scales. A score is defined to be the gradient of the log density with respect to the input data. Our methodology is completely unsupervised and follows a straight forward training sche... | MULTISCALE SCORE MATCHING FOR OUT-OF- DISTRIBUTION DETECTION |
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