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d17262272 | Recently deep neural networks have received considerable attention due to their ability to extract and represent high-level abstractions in data sets. Deep neural networks such as fully-connected and convolutional neural networks have shown excellent performance on a wide range of recognition and classification tasks. ... | SPARSELY-CONNECTED NEURAL NETWORKS: TO- WARDS EFFICIENT VLSI IMPLEMENTATION OF DEEP NEURAL NETWORKS |
d204800400 | The long-tail distribution of the visual world poses great challenges for deep learning based classification models on how to handle the class imbalance problem. Existing solutions usually involve class-balancing strategies, e.g. by loss re-weighting, data re-sampling, or transfer learning from head-to tail-classes, bu... | Published as a conference paper at ICLR 2020 DECOUPLING REPRESENTATION AND CLASSIFIER FOR LONG-TAILED RECOGNITION |
d251649215 | We consider learning causal relationships under conditional moment restrictions. Unlike causal inference under unconditional moment restrictions, conditional moment restrictions pose serious challenges for causal inference, especially in highdimensional settings. To address this issue, we propose a method that transfor... | LEARNING CAUSAL MODELS FROM CONDITIONAL MOMENT RESTRICTIONS BY IMPORTANCE WEIGHT- ING |
d256827458 | We introduce VA-DepthNet, a simple, effective, and accurate deep neural network approach for the single-image depth prediction (SIDP) problem. The proposed approach advocates using classical first-order variational constraints for this problem. While state-of-the-art deep neural network methods for SIDP learn the scene... | VA-DEPTHNET: A VARIATIONAL APPROACH TO SIN- GLE IMAGE DEPTH PREDICTION |
d257102833 | This paper shows that the heterogeneity in neuronal and synaptic dynamics reduces the spiking activity of a Recurrent Spiking Neural Network (RSNN) while improving prediction performance, enabling spike-efficient (unsupervised) learning. We analytically show that the diversity in neurons' integration/relaxation dynamic... | HETEROGENEOUS NEURONAL AND SYNAPTIC DYNAM- ICS FOR SPIKE-EFFICIENT UNSUPERVISED LEARNING: THEORY AND DESIGN PRINCIPLES |
d2768038 | We consider learning representations of entities and relations in KBs using the neural-embedding approach. We show that most existing models, including NTN (Socher et al., 2013) and TransE (Bordes et al., 2013b), can be generalized under a unified learning framework, where entities are low-dimensional vectors learned ... | Under review as conference paper at ICLR 2015 EMBEDDING ENTITIES AND RELATIONS FOR LEARN- ING AND INFERENCE IN KNOWLEDGE BASES |
d221970551 | A data set sampled from a certain population is biased if the subgroups of the population are sampled at proportions that are significantly different from their underlying proportions. Training machine learning models on biased data sets requires correction techniques to compensate for the bias. We consider two commonl... | Published as a conference paper at ICLR 2021 WHY RESAMPLING OUTPERFORMS REWEIGHTING FOR CORRECTING SAMPLING BIAS WITH STOCHASTIC GRA- DIENTS |
d254408495 | Changing how pre-trained models behave-e.g., improving their performance on a downstream task or mitigating biases learned during pre-training-is a common practice when developing machine learning systems. In this work, we propose a new paradigm for steering the behavior of neural networks, centered around task vectors... | Published as a conference paper at ICLR 2023 EDITING MODELS WITH TASK ARITHMETIC |
d6021932 | We present experiments demonstrating that some other form of capacity control, different from network size, plays a central role in learning multi-layer feedforward networks. We argue, partially through analogy to matrix factorization, that this is an inductive bias that can help shed light on deep learning. | IN SEARCH OF THE REAL INDUCTIVE BIAS: ON THE ROLE OF IMPLICIT REGULARIZATION IN DEEP LEARNING |
d257255248 | Building systems that achieve a deeper understanding of language is one of the central goals of natural language processing (NLP). Towards this goal, recent works have begun to train language models on narrative datasets which require extracting the most critical information by integrating across long contexts. However... | Published as a conference paper at ICLR 2023 TRAINING LANGUAGE MODELS TO SUMMARIZE NARRATIVES IMPROVES BRAIN ALIGNMENT |
d247518913 | Searching for the architecture cells is a dominant paradigm in NAS. However, little attention has been devoted to the analysis of the cell-based search spaces even though it is highly important for the continual development of NAS. In this work, we conduct an empirical post-hoc analysis of architectures from the popula... | Published as a conference paper at ICLR 2022 ON REDUNDANCY AND DIVERSITY IN CELL-BASED NEURAL ARCHITECTURE SEARCH |
d16563349 | Deep Belief Networks which are hierarchical generative models are effective tools for feature representation and extraction. Furthermore, DBNs can be used in numerous aspects of Machine Learning such as image denoising. In this paper, we propose a novel method for image denoising which relies on the DBNs' ability in fe... | Deep Belief Networks for Image Denoising |
d204509670 | Neural ordinary differential equations (ODEs) have been attracting increasing attention in various research domains recently. There have been some works studying optimization issues and approximation capabilities of neural ODEs, but their robustness is still yet unclear. In this work, we fill this important gap by expl... | ON ROBUSTNESS OF NEURAL ORDINARY DIFFEREN- TIAL EQUATIONS |
d257279790 | Large pre-trained language models help to achieve state of the art on a variety of natural language processing (NLP) tasks, nevertheless, they still suffer from forgetting when incrementally learning a sequence of tasks. To alleviate this problem, recent works enhance existing models by sparse experience replay and loc... | CAN BERT REFRAIN FROM FORGETTING ON SEQUEN- TIAL TASKS? A PROBING STUDY |
d214014558 | We study reinforcement learning in settings where sampling an action from the policy must be done concurrently with the time evolution of the controlled system, such as when a robot must decide on the next action while still performing the previous action. Much like a person or an animal, the robot must think and move ... | Published as a conference paper at ICLR 2020 THINKING WHILE MOVING: DEEP REINFORCEMENT LEARNING WITH CONCURRENT CONTROL |
d16305497 | Competing with top human players in the ancient game of Go has been a longterm goal of artificial intelligence. Go's high branching factor makes traditional search techniques ineffective, even on leading-edge hardware, and Go's evaluation function could change drastically with one stone change. Recent works[Maddison et... | BETTER COMPUTER GO PLAYER WITH NEURAL NET- WORK AND LONG-TERM PREDICTION |
d189999405 | As the performance of computer systems stagnates due to the end of Moore's Law, there is a need for new models that can understand and optimize the execution of general purpose code. While there is a growing body of work on using Graph Neural Networks (GNNs) to learn static representations of source code, these represe... | Published as a conference paper at ICLR 2020 LEARNING EXECUTION THROUGH NEURAL CODE FUSION |
d246294455 | In Multiple Instance Learning (MIL), models are trained using bags of instances, where only a single label is provided for each bag. A bag label is often only determined by a handful of key instances within a bag, making it difficult to interpret what information a classifier is using to make decisions. In this work, w... | Published as a conference paper at ICLR 2022 MODEL AGNOSTIC INTERPRETABILITY FOR MULTIPLE INSTANCE LEARNING |
d228063783 | Predicting the dynamics of neural network parameters during training is one of the key challenges in building a theoretical foundation for deep learning.A central obstacle is that the motion of a network in high-dimensional parameter space undergoes discrete finite steps along complex stochastic gradients derived from ... | NEURAL MECHANICS: SYMMETRY AND BROKEN CON-SERVATION LAWS IN DEEP LEARNING DYNAMICS |
d248987713 | Knowledge Distillation (KD) is a typical method for training a lightweight student model with the help of a well-trained teacher model. However, most KD methods require access to either the teacher's training data or model parameter, which is unrealistic. To tackle this problem, recent works study KD under data-free an... | Published as a conference paper at ICLR 2023 IDEAL: QUERY-EFFICIENT DATA-FREE LEARNING FROM BLACK-BOX MODELS |
d3299269 | Training deep neural networks (DNNs) efficiently is a challenge due to the associated highly nonconvex optimization. The backpropagation (backprop) algorithm has long been the most widely used algorithm for gradient computation of parameters of DNNs and is used along with gradient descent-type algorithms for this optim... | Workshop track -ICLR 2018 A PROXIMAL BLOCK COORDINATE DESCENT ALGO- RITHM FOR DEEP NEURAL NETWORK TRAINING |
d49666783 | Model-based reinforcement learning (RL) is considered to be a promising approach to reduce the sample complexity that hinders model-free RL. However, the theoretical understanding of such methods has been rather limited. This paper introduces a novel algorithmic framework for designing and analyzing model-based RL algo... | ALGORITHMIC FRAMEWORK FOR MODEL-BASED DEEP REINFORCEMENT LEARNING WITH THEORETI- CAL GUARANTEES |
d14949670 | Natural language generation plays a critical role in any spoken dialogue system. We present a new approach to natural language generation using recurrent neural networks in an encoderdecoder framework. In contrast with previous work, our model uses both lexicalized and delexicalized versions of slot-value pairs for eac... | Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data |
d235294221 | Graph generative models are a highly active branch of machine learning. Given the steady development of new models of ever-increasing complexity, it is necessary to provide a principled way to evaluate and compare them. In this paper, we enumerate the desirable criteria for such a comparison metric and provide an overv... | Published as a conference paper at ICLR 2022 EVALUATION METRICS FOR GRAPH GENERATIVE MODELS: PROBLEMS, PITFALLS, AND PRACTICAL SOLUTIONS |
d9545399 | We systematically explore regularizing neural networks by penalizing low entropy output distributions. We show that penalizing low entropy output distributions, which has been shown to improve exploration in reinforcement learning, acts as a strong regularizer in supervised learning. Furthermore, we connect a maximum e... | REGULARIZING NEURAL NETWORKS BY PENALIZING CONFIDENT OUTPUT DISTRIBUTIONS |
d257505014 | We consider the task of generating realistic 3D shapes, which is useful for a variety of applications such as automatic scene generation and physical simulation. Compared to other 3D representations like voxels and point clouds, meshes are more desirable in practice, because (1) they enable easy and arbitrary manipulat... | Published as a conference paper at ICLR 2023 MESHDIFFUSION: SCORE-BASED GENERATIVE 3D MESH MODELING |
d232273817 | Many complex real-world tasks are composed of several levels of sub-tasks. Humans leverage these hierarchical structures to accelerate the learning process and achieve better generalization. In this work, we study the inductive bias and propose Ordered Memory Policy Network (OMPN) to discover subtask hierarchy by learn... | Published as a conference paper at ICLR 2021 LEARNING TASK DECOMPOSITION WITH ORDERED MEMORY POLICY NETWORK |
d252846193 | Work on fast weight programmers has demonstrated the effectiveness of key/value outer product-based learning rules for sequentially generating a weight matrix (WM) of a neural net (NN) by another NN or itself. However, the weight generation steps are typically not visually interpretable by humans, because the contents ... | Published as a conference paper at ICLR 2023 IMAGES AS WEIGHT MATRICES: SEQUENTIAL IMAGE GENERATION THROUGH SYNAPTIC LEARNING RULES |
d257019892 | With the increasing amount of multimedia data on modern mobile systems and IoT infrastructures, harnessing these rich multimodal data without breaching user privacy becomes a critical issue. Federated learning (FL) serves as a privacyconscious alternative to centralized machine learning. However, existing FL methods ex... | MULTIMODAL FEDERATED LEARNING VIA CON- TRASTIVE REPRESENTATION ENSEMBLE |
d234096198 | Network pruning is an effective method to reduce the computational expense of over-parameterized neural networks for deployment on low-resource systems. Recent state-of-the-art techniques for retraining pruned networks such as weight rewinding and learning rate rewinding have been shown to outperform the traditional fi... | Published as a conference paper at ICLR 2021 NETWORK PRUNING THAT MATTERS: A CASE STUDY ON RETRAINING VARIANTS |
d4735139 | In dynamic malware analysis, programs are classified as malware or benign based on their execution logs. We propose a concept of applying monotonic classification models to the analysis process, to make the trained model's predictions consistent over execution time and provably stable to the injection of any noise or '... | Workshop track -ICLR 2018 MONOTONIC MODELS FOR REAL-TIME DYNAMIC MALWARE DETECTION |
d252110963 | Latent manifolds provide a compact characterization of neural population activity and of shared co-variability across brain areas. Nonetheless, existing statistical tools for extracting neural manifolds face limitations in terms of interpretability of latents with respect to task variables, and can be hard to apply to ... | A probabilistic framework for task-aligned intra-and inter-area neural manifold estimation |
d118696157 | Learning to take actions based on observations is a core requirement for artificial agents to be able to be successful and robust at their task. Reinforcement Learning (RL) is a well-known technique for learning such policies. However, current RL algorithms often have to deal with reward shaping, have difficulties gene... | BAYESIAN POLICY SELECTION USING ACTIVE INFERENCE |
d258079367 | Variational autoencoders (VAEs) are powerful generative modelling methods, however they suffer from blurry generated samples and reconstructions compared to the images they have been trained on. Significant research effort has been spent to increase the generative capabilities by creating more flexible models but often... | EXPLICITLY MINIMIZING THE BLUR ERROR OF VARI- ATIONAL AUTOENCODERS |
d256808752 | The vulnerability of deep neural networks (DNNs) to adversarial examples has attracted great attention in the machine learning community. The problem is related to non-flatness and non-smoothness of normally obtained loss landscapes. Training augmented with adversarial examples (a.k.a., adversarial training) is consid... | Published as a conference paper at ICLR 2023 SQUEEZE TRAINING FOR ADVERSARIAL ROBUSTNESS |
d257220031 | Federated Learning (FL) aims to develop a centralized server that learns from distributed clients via communications without accessing the clients' local data. However, existing works mainly focus on federated learning in a single task scenario. with static data. In this paper, we introduce the continual federated lear... | Published as a conference paper at ICLR 2023 BETTER GENERATIVE REPLAY FOR CONTINUAL FEDERATED LEARNING |
d257019561 | Pretrained language models have shown superior performance on many natural language processing tasks, yet they still struggle at multi-step formal reasoning tasks like grade school math problems. One key challenge of finetuning them to solve such math reasoning problems is that many existing datasets only contain one r... | Published as a conference paper at ICLR 2023 LEARNING MATH REASONING FROM SELF-SAMPLED CORRECT AND PARTIALLY-CORRECT SOLUTIONS |
d8419990 | We investigate the problem of inducing word embeddings that are tailored for a particular bilexical relation. Our learning algorithm takes an existing lexical vector space and compresses it such that the resulting word embeddings are good predictors for a target bilexical relation. In experiments we show that task-spec... | Under review as a workshop contribution at ICLR 2015 TAILORING WORD EMBEDDINGS FOR BILEXICAL PREDICTIONS: AN EXPERIMENTAL COMPARISON |
d232290796 | HardWare-aware Neural Architecture Search (HW-NAS) has recently gained tremendous attention by automating the design of deep neural networks deployed in more resource-constrained daily life devices. Despite its promising performance, developing optimal HW-NAS solutions can be prohibitively challenging as it requires cr... | HW-NAS-BENCH: HARDWARE-AWARE NEURAL AR- CHITECTURE SEARCH BENCHMARK |
d67856619 | Transfer learning aims to solve the data sparsity for a target domain by applying information of the source domain. Given a sequence (e.g. a natural language sentence), the transfer learning, usually enabled by recurrent neural network (RNN), represents the sequential information transfer. RNN uses a chain of repeating... | TRANSFER LEARNING FOR SEQUENCES VIA LEARN- ING TO COLLOCATE |
d237940779 | In NLP, a large volume of tasks involve pairwise comparison between two sequences (e.g., sentence similarity and paraphrase identification). Predominantly, two formulations are used for sentence-pair tasks: bi-encoders and cross-encoders. Bi-encoders produce fixed-dimensional sentence representations and are computatio... | TRANS-ENCODER: UNSUPERVISED SENTENCE-PAIR MODELLING THROUGH SELF-AND MUTUAL-DISTILLATIONS |
d259298795 | We show that participating in federated learning can be detrimental to group fairness. In fact, the bias of a few parties against under-represented groups (identified by sensitive attributes such as gender or race) can propagate through the network to all the parties in the network. We analyze and explain bias propagat... | Published as a conference paper at ICLR 2023 BIAS PROPAGATION IN FEDERATED LEARNING |
d235421834 | Randomized Smoothing (RS) is a promising method for obtaining robustness certificates by evaluating a base model under noise. In this work, we: (i) theoretically motivate why ensembles are a particularly suitable choice as base models for RS, and (ii) empirically confirm this choice, obtaining state-of-the-art results ... | Published as a conference paper at ICLR 2022 BOOSTING RANDOMIZED SMOOTHING WITH VARIANCE REDUCED CLASSIFIERS |
d195833273 | This paper studies the expressive power of graph neural networks falling within the message-passing framework (GNN mp ). Two results are presented. First, GNN mp are shown to be Turing universal under sufficient conditions on their depth, width, node attributes, and layer expressiveness. Second, it is discovered that G... | Published as a conference paper at ICLR 2020 WHAT GRAPH NEURAL NETWORKS CANNOT LEARN: DEPTH VS WIDTH |
d252734947 | Despite the use of large vision and language models (VLMs) in many downstream applications, it is unclear how well they encode the compositional relationships between objects and attributes. Here, we create the Attribution, Relation, and Order (ARO) benchmark to systematically evaluate the ability of VLMs to understand... | Published as a conference paper at ICLR 2023 WHEN AND WHY VISION-LANGUAGE MODELS BE- HAVE LIKE BAGS-OF-WORDS, AND WHAT TO DO ABOUT IT? |
d222142219 | Recent increases in the computational demands of deep neural networks (DNNs), combined with the observation that most input samples require only simple models, have sparked interest in input-adaptive multi-exit architectures, such as MSDNets or Shallow-Deep Networks. These architectures enable faster inferences and cou... | A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference A PANDA? NO, IT'S A SLOTH: SLOWDOWN ATTACKS ON ADAPTIVE MULTI-EXIT NEURAL NETWORK INFERENCE |
d53366105 | Published as a conference paper at ICLR 2019 | |
d232307121 | Machine learning (ML) models that learn and predict properties of computer programs are increasingly being adopted and deployed. In this work, we investigate principled ways to adversarially perturb a computer program to fool such learned models, and thus determine their adversarial robustness. We use program obfuscati... | Published as a conference paper at ICLR 2021 GENERATING ADVERSARIAL COMPUTER PROGRAMS USING OPTIMIZED OBFUSCATIONS |
d252968113 | We introduce Token Merging (ToMe), a simple method to increase the throughput of existing ViT models without needing to train. ToMe gradually combines similar tokens in a transformer using a general and light-weight matching algorithm that is as fast as pruning while being more accurate. Off-the-shelf, ToMe can 2× the ... | Published as a conference paper at ICLR 2023 TOKEN MERGING: YOUR VIT BUT FASTER |
d233394003 | Large optimization problems with hard constraints arise in many settings, yet classical solvers are often prohibitively slow, motivating the use of deep networks as cheap "approximate solvers." Unfortunately, naive deep learning approaches typically cannot enforce the hard constraints of such problems, leading to infea... | Published as a conference paper at ICLR 2021 DC3: A LEARNING METHOD FOR OPTIMIZATION WITH HARD CONSTRAINTS |
d253107402 | Message Passing Neural Networks (MPNNs) are a widely used class of Graph Neural Networks (GNNs). The limited representational power of MPNNs inspires the study of provably powerful GNN architectures. However, knowing one model is more powerful than another gives little insight about what functions they can or cannot ex... | Published as a conference paper at ICLR 2023 BOOSTING THE CYCLE COUNTING POWER OF GRAPH NEURAL NETWORKS WITH I 2 -GNNS |
d3619097 | Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems, including agents that can move with skill and agility through their environment. An open problem in this setting is that of developing good strategies for integrating or merging policies for multiple skills, where each... | Published as a conference paper at ICLR 2018 PROGRESSIVE REINFORCEMENT LEARNING WITH DISTILLATION FOR MULTI-SKILLED MOTION CONTROL |
d202712891 | We study the problem of defending deep neural network approaches for image classification from physically realizable attacks. First, we demonstrate that the two most scalable and effective methods for learning robust models, adversarial training with PGD attacks and randomized smoothing, exhibit limited effectiveness a... | Published as a conference paper at ICLR 2020 DEFENDING AGAINST PHYSICALLY REALIZABLE AT- TACKS ON IMAGE CLASSIFICATION |
d170078913 | Neural networks have succeeded in many reasoning tasks. Empirically, these tasks require specialized network structures, e.g., Graph Neural Networks (GNNs) perform well on many such tasks, but less structured networks fail. Theoretically, there is limited understanding of why and when a network structure generalizes be... | Published as a conference paper at ICLR 2020 WHAT CAN NEURAL NETWORKS REASON ABOUT? |
d256827816 | Modern semantic segmentation methods devote much effect to adjusting image feature representations to improve the segmentation performance in various ways, such as architecture design, attention mechnism, etc. However, almost all those methods neglect the particularity of class weights (in the classification layer) in ... | E-CRF: EMBEDDED CONDITIONAL RANDOM FIELD FOR BOUNDARY-CAUSED CLASS WEIGHTS CONFU- SION IN SEMANTIC SEGMENTATION |
d209476324 | In this paper, we propose an end-to-end deep learning model, called E2Efold, for RNA secondary structure prediction which can effectively take into account the inherent constraints in the problem. The key idea of E2Efold is to directly predict the RNA base-pairing matrix, and use an unrolled algorithm for constrained p... | Published as a conference paper at ICLR 2020 RNA SECONDARY STRUCTURE PREDICTION BY LEARNING UNROLLED ALGORITHMS |
d252780545 | Recent advances in pre-training vision-language models like CLIP(Radford et al., 2021)have shown great potential in learning transferable visual representations. Nonetheless, for downstream inference, CLIP-like models suffer from either 1) degraded accuracy and robustness in the case of inaccurate text descriptions dur... | Under review as a conference paper at ICLR 2023 LEARNING TO DECOMPOSE VISUAL FEATURES WITH LATENT TEXTUAL PROMPTS |
d232232790 | Motivated by the rising abundance of observational data with continuous treatments, we investigate the problem of estimating the average dose-response curve (ADRF). Available parametric methods are limited in their model space, and previous attempts in leveraging neural network to enhance model expressiveness relied on... | Published as a conference paper at ICLR 2021 VCNET AND FUNCTIONAL TARGETED REGULARIZA- TION FOR LEARNING CAUSAL EFFECTS OF CONTINU- OUS TREATMENTS |
d211132680 | We address the problem of discovering 3D parts for objects in unseen categories. | Published as a conference paper at ICLR 2020 LEARNING TO GROUP: A BOTTOM-UP FRAMEWORK FOR 3D PART DISCOVERY IN UNSEEN CATEGORIES |
d238634570 | Normalizing flows have shown great success as general-purpose density estimators. However, many real world applications require the use of domain-specific knowledge, which normalizing flows cannot readily incorporate. We propose embedded-model flows (EMF), which alternate general-purpose transformations with structured... | EMBEDDED-MODEL FLOWS: COMBINING THE INDUC- TIVE BIASES OF MODEL-FREE DEEP LEARNING AND EXPLICIT PROBABILISTIC MODELING |
d256808454 | The transferability of adversarial examples across deep neural networks (DNNs) is the crux of many black-box attacks. Many prior efforts have been devoted to improving the transferability via increasing the diversity in inputs of some substitute models. In this paper, by contrast, we opt for the diversity in substitute... | Published as a conference paper at ICLR 2023 MAKING SUBSTITUTE MODELS MORE BAYESIAN CAN ENHANCE TRANSFERABILITY OF ADVERSARIAL EX- AMPLES |
d246210292 | Information sharing is key in building team cognition and enables coordination and cooperation. High-performing human teams also benefit from acting strategically with hierarchical levels of iterated communication and rationalizability, meaning a human agent can reason about the actions of their teammates in their deci... | Published as a conference paper at ICLR 2022 ITERATED REASONING WITH MUTUAL INFORMATION IN COOPERATIVE AND BYZANTINE DECENTRALIZED TEAMING |
d259108523 | This paper aims to deal with the ignored real-world complexities in prior work on human motion forecasting, emphasizing the social properties of multi-person motion, the diversity of motion and social interactions, and the complexity of articulated motion. To this end, we introduce a novel task of stochastic multi-pers... | STOCHASTIC MULTI-PERSON 3D MOTION FORECAST- ING |
d67364912 | Recent efforts on combining deep models with probabilistic graphical models are promising in providing flexible models that are also easy to interpret. We propose a variational message-passing algorithm for variational inference in such models. We make three contributions. First, we propose structured inference network... | Published as a conference paper at ICLR 2018 VARIATIONAL MESSAGE PASSING WITH STRUCTURED INFERENCE NETWORKS |
d220403547 | We investigate two causes for adversarial vulnerability in deep neural networks: bad data and (poorly) trained models. When trained with SGD, deep neural networks essentially achieve zero training error, even in the presence of label noise, while also exhibiting good generalization on natural test data, something refer... | How benign is benign overfitting? |
d4109354 | Workshop track -ICLR 2018 COMPARING FIXED AND ADAPTIVE COMPUTATION TIME FOR RECURRENT NEURAL NETWORKS | |
d231855774 | Neural ordinary differential equations (Neural ODEs) are a new family of deeplearning models with continuous depth. However, the numerical estimation of the gradient in the continuous case is not well solved: existing implementations of the adjoint method suffer from inaccuracy in reverse-time trajectory, while the nai... | MALI: A MEMORY EFFICIENT AND REVERSE ACCU- RATE INTEGRATOR FOR NEURAL ODES |
d10691002 | We show that deep narrow Boltzmann machines are universal approximators of probability distributions on the activities of their visible units, provided they have sufficiently many hidden layers, each containing the same number of units as the visible layer. Besides from this existence statement, we provide upper and lo... | Deep Narrow Boltzmann Machines are Universal Approximators |
d3515469 | Permutations and matchings are core building blocks in a variety of latent variable models, as they allow us to align, canonicalize, and sort data. Learning in such models is difficult, however, because exact marginalization over these combinatorial objects is intractable. In response, this paper introduces a collectio... | Published as a conference paper at ICLR 2018 LEARNING LATENT PERMUTATIONS WITH GUMBEL- SINKHORN NETWORKS |
d247158892 | A standard hardware bottleneck when training deep neural networks is GPU memory. The bulk of memory is occupied by caching intermediate tensors for gradient computation in the backward pass. We propose a novel method to reduce this footprint -Dropping Intermediate Tensors (DropIT). DropIT drops min-k elements of the in... | Published as a conference paper at ICLR 2023 DROPIT: DROPPING INTERMEDIATE TENSORS FOR MEMORY-EFFICIENT DNN TRAINING |
d256358479 | Existing Deep Reinforcement Learning (DRL) algorithms suffer from sample inefficiency. Generally, episodic control-based approaches are solutions that leverage highly-rewarded past experiences to improve sample efficiency of DRL algorithms. However, previous episodic control-based approaches fail to utilize the latent ... | Published as a conference paper at ICLR 2023 NEURAL EPISODIC CONTROL WITH STATE ABSTRAC- TION |
d238407848 | Biological spiking neural networks (SNNs) can temporally encode information in their outputs, e.g. in the rank order in which neurons fire, whereas artificial neural networks (ANNs) conventionally do not. As a result, models of SNNs for neuromorphic computing are regarded as potentially more rapid and efficient than AN... | SPIKE-INSPIRED RANK CODING FOR FAST AND ACCU- RATE RECURRENT NEURAL NETWORKS |
d257280243 | Recently, there has been a growing surge of interest in enabling machine learning systems to generalize well to Out-of-Distribution (OOD) data. Most efforts are devoted to advancing optimization objectives that regularize models to capture the underlying invariance; however, there often are compromises in the optimizat... | PARETO INVARIANT RISK MINIMIZATION: TOWARDS MITIGATING THE OPTIMIZATION DILEMMA IN OUT- OF-DISTRIBUTION GENERALIZATION |
d256827141 | In recent years, deep network pruning has attracted significant attention in order to enable the rapid deployment of AI into small devices with computation and memory constraints. Pruning is often achieved by dropping redundant weights, neurons, or layers of a deep network while attempting to retain a comparable test p... | Published as a conference paper at ICLR 2023 PRUNING DEEP NEURAL NETWORKS FROM A SPARSITY PERSPECTIVE |
d198917339 | The problem of verifying whether a textual hypothesis holds the truth based on the given evidence, also known as fact verification, plays an important role in the study of natural language understanding and semantic representation. However, existing studies are mainly restricted to dealing with unstructured evidence (e... | TABFACT: A LARGE-SCALE DATASET FOR TABLE- BASED FACT VERIFICATION |
d256194556 | Adversarial training suffers from the issue of robust overfitting, which seriously impairs its generalization performance. Data augmentation, which is effective at preventing overfitting in standard training, has been observed by many previous works to be ineffective in mitigating overfitting in adversarial training. T... | Published as a conference paper at ICLR 2023 DATA AUGMENTATION ALONE CAN IMPROVE ADVER- SARIAL TRAINING |
d247011539 | Denoising diffusion probabilistic models have been recently proposed to generate high-quality samples by estimating the gradient of the data density. The framework defines the prior noise as a standard Gaussian distribution, whereas the corresponding data distribution may be more complicated than the standard Gaussian ... | Published as a conference paper at ICLR 2022 PRIORGRAD: IMPROVING CONDITIONAL DENOISING DIFFUSION MODELS WITH DATA-DEPENDENT ADAP- TIVE PRIOR |
d252596186 | Vessel segmentation in medical images is one of the important tasks in the diagnosis of vascular diseases and therapy planning. Although learning-based segmentation approaches have been extensively studied, a large amount of groundtruth labels are required in supervised methods and confusing background structures make ... | DIFFUSION ADVERSARIAL REPRESENTATION LEARN- ING FOR SELF-SUPERVISED VESSEL SEGMENTATION |
d258841675 | Existing feature distillation methods commonly adopt the One-to-one Representation Matching between any pre-selected teacher-student layer pair. In this paper, we present N-to-One Representation Matching (NORM), a new two-stage knowledge distillation method, which relies on a simple Feature Transform (FT) module consis... | Published as a conference paper at ICLR 2023 NORM: KNOWLEDGE DISTILLATION VIA N-TO-ONE REPRESENTATION MATCHING |
d257426979 | Learning policies via preference-based reward learning is an increasingly popular method for customizing agent behavior, but has been shown anecdotally to be prone to spurious correlations and reward hacking behaviors. While much prior work focuses on causal confusion in reinforcement learning and behavioral cloning, w... | Published as a conference paper at ICLR 2023 CAUSAL CONFUSION AND REWARD MISIDENTIFICA- TION IN PREFERENCE-BASED REWARD LEARNING |
d231861390 | We study the challenging task of neural network quantization without end-toend retraining, called Post-training Quantization (PTQ). PTQ usually requires a small subset of training data but produces less powerful quantized models than Quantization-Aware Training (QAT). In this work, we propose a novel PTQ framework, dub... | Published as a conference paper at ICLR 2021 BRECQ: PUSHING THE LIMIT OF POST-TRAINING QUANTIZATION BY BLOCK RECONSTRUCTION |
d248476477 | We propose a novel 3d shape representation for 3d shape reconstruction from a single image. Rather than predicting a shape directly, we train a network to generate a training set which will be fed into another learning algorithm to define the shape. The nested optimization problem can be modeled by bi-level optimizatio... | Published as a conference paper at ICLR 2022 TRAINING DATA GENERATING NETWORKS: SHAPE RECONSTRUCTION VIA BI-LEVEL OPTIMIZATION |
d253098549 | The formalization of existing mathematical proofs is a notoriously difficult process. Despite decades of research on automation and proof assistants, writing formal proofs remains arduous and only accessible to a few experts. While previous studies to automate formalization focused on powerful search algorithms, no att... | Published as a conference paper at ICLR 2023 DRAFT, SKETCH, AND PROVE: GUIDING FORMAL THEOREM PROVERS WITH INFORMAL PROOFS |
d18323900 | Several interesting generative learning algorithms involve a complex probability distribution over many random variables, involving intractable normalization constants or latent variable marginalization. Some of them may not have even an analytic expression for the unnormalized probability function and no tractable app... | Bounding the Test Log-Likelihood of Generative Models |
d2536452 | We propose a method for learning latent representations of the factors of variation in data. By augmenting deep autoencoders with a supervised cost and an additional unsupervised cost, we create a semi-supervised model that can discover and explicitly represent factors of variation beyond those relevant for categorizat... | DISCOVERING HIDDEN FACTORS OF VARIATION IN DEEP NETWORKS |
d257482840 | We consider the problem of training a deep neural network on a given classification task, e.g., ImageNet-1K (IN1K), so that it excels at both the training task as well as at other (future) transfer tasks. These two seemingly contradictory properties impose a trade-off between improving the model's generalization and ma... | Published as a conference paper at ICLR 2023 NO REASON FOR NO SUPERVISION: IMPROVED GENERALIZATION IN SUPERVISED MODELS |
d237347130 | Since the introduction of the transformer model byVaswani et al. (2017), a fundamental question has yet to be answered: how does a model achieve extrapolation at inference time for sequences that are longer than it saw during training? We first show that extrapolation can be enabled by simply changing the position repr... | Published as a conference paper at ICLR 2022 TRAIN SHORT, TEST LONG: ATTENTION WITH LINEAR BIASES ENABLES INPUT LENGTH EXTRAPOLATION |
d53113692 | Neural language models have been widely used in various NLP tasks, including machine translation, next word prediction and conversational agents. However, it is challenging to deploy these models on mobile devices due to their slow prediction speed, where the bottleneck is to compute top candidates in the softmax layer... | LEARNING TO SCREEN FOR FAST SOFTMAX INFER- ENCE ON LARGE VOCABULARY NEURAL NETWORKS |
d248006494 | We introduce compositional soft prompting (CSP), a parameter-efficient learning technique to improve the zero-shot compositionality of large-scale pretrained vision-language models (VLMs) like CLIP. We develop CSP for compositional zero-shot learning, the task of predicting unseen attribute-object compositions (e.g., o... | Published as a conference paper at ICLR 2023 LEARNING TO COMPOSE SOFT PROMPTS FOR COMPOSITIONAL ZERO-SHOT LEARNING |
d252668546 | Dedicated neural network (NN) architectures have been designed to handle specific data types (such as CNN for images or RNN for text), which ranks them among state-of-the-art methods for dealing with these data. Unfortunately, no architecture has been found for dealing with tabular data yet, for which tree ensemble met... | S T - I N N |
d7301499 | Transferring knowledge from prior source tasks in solving a new target task can be useful in several learning applications. The application of transfer poses two serious challenges which have not been adequately addressed. First, the agent should be able to avoid negative transfer, which happens when the transfer hampe... | Published as a conference paper at ICLR 2017 ATTEND, ADAPT AND TRANSFER: ATTENTIVE DEEP ARCHITECTURE FOR ADAPTIVE TRANSFER FROM MULTIPLE SOURCES IN THE SAME DOMAIN |
d231627780 | Given (small amounts of) time-series' data from a high-dimensional, fine-grained, multiscale dynamical system, we propose a generative framework for learning an effective, lower-dimensional, coarse-grained dynamical model that is predictive of the fine-grained system's long-term evolution but also of its behavior under... | PHYSICS-AWARE, PROBABILISTIC MODEL ORDER RE- DUCTION WITH GUARANTEED STABILITY |
d232257725 | The ability to learn continually without forgetting the past tasks is a desired attribute for artificial learning systems. Existing approaches to enable such learning in artificial neural networks usually rely on network growth, importance based weight update or replay of old data from the memory. In contrast, we propo... | Published as a conference paper at ICLR 2021 GRADIENT PROJECTION MEMORY FOR CONTINUAL LEARNING |
d235652429 | Data augmentation is a key element of deep learning pipelines, as it informs the network during training about transformations of the input data that keep the label unchanged. Manually finding adequate augmentation methods and parameters for a given pipeline is however rapidly cumbersome. In particular, while intuition... | CADDA: Class-wise Automatic Differen- tiable Data Augmentation for EEG Signals |
d254043898 | Neural networks with sinusoidal activations have been proposed as an alternative to networks with traditional activation functions. Despite their promise, particularly for learning implicit models, their training behavior is not yet fully understood, leading to a number of empirical design choices that are not well jus... | Published as a conference paper at ICLR 2023 SIMPLE INITIALIZATION AND PARAMETRIZATION OF SINUSOIDAL NETWORKS VIA THEIR KERNEL BAND- WIDTH |
d6395440 | There are two main approaches to the distributed representation of words: lowdimensional deep learning embeddings and high-dimensional distributional models, in which each dimension corresponds to a context word. In this paper, we combine these two approaches by learning embeddings based on distributionalmodel vectors ... | Distributional Models and Deep Learning Embeddings: Combining the Best of Both Worlds |
d211132734 | The universal approximation theorem, in one of its most general versions, says that if we consider only continuous activation functions σ, then a standard feedforward neural network with one hidden layer is able to approximate any continuous multivariate function f to any given approximation threshold ε, if and only if... | A CLOSER LOOK AT THE APPROXIMATION CAPABILI- TIES OF NEURAL NETWORKS |
d6679306 | Multiple instance learning (MIL) can reduce the need for costly annotation in tasks such as semantic segmentation by weakening the required degree of supervision. We propose a novel MIL formulation of multi-class semantic segmentation learning by a fully convolutional network. In this setting, we seek to learn a semant... | Under review as a workshop contribution at ICLR 2015 FULLY CONVOLUTIONAL MULTI-CLASS MULTIPLE INSTANCE LEARNING |
d222177084 | We propose Deep Autoencoding Predictive Components (DAPC) -a selfsupervised representation learning method for sequence data, based on the intuition that useful representations of sequence data should exhibit a simple structure in the latent space. We encourage this latent structure by maximizing an estimate of predict... | REPRESENTATION LEARNING FOR SEQUENCE DATA WITH DEEP AUTOENCODING PREDICTIVE COMPO- NENTS |
d249209758 | We introduce CriticSMC, a new algorithm for planning as inference built from a composition of sequential Monte Carlo with learned Soft-Q function heuristic factors. These heuristic factors, obtained from parametric approximations of the marginal likelihood ahead, more effectively guide SMC towards the desired target di... | Published as a conference paper at ICLR 2023 CRITIC SEQUENTIAL MONTE CARLO |
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