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d238354231 | We introduce Noisy Feature Mixup (NFM), an inexpensive yet effective method for data augmentation that combines the best of interpolation based training and noise injection schemes. Rather than training with convex combinations of pairs of examples and their labels, we use noise-perturbed convex combinations of pairs o... | Noisy Feature Mixup |
d226278174 | Neural link predictors are immensely useful for identifying missing edges in large scale Knowledge Graphs. However, it is still not clear how to use these models for answering more complex queries that arise in a number of domains, such as queries using logical conjunctions (∧), disjunctions (∨) and existential quantif... | COMPLEX QUERY ANSWERING WITH NEURAL LINK PREDICTORS |
d249240397 | Recent advances in the understanding of Generative Adversarial Networks (GANs) have led to remarkable progress in visual editing and synthesis tasks, capitalizing on the rich semantics that are embedded in the latent spaces of pre-trained GANs. However, existing methods are often tailored to specific GAN architectures ... | PANDA: UNSUPERVISED LEARNING OF PARTS AND APPEARANCES IN THE FEATURE MAPS OF GANS |
d52895409 | Adversarial learning methods have been proposed for a wide range of applications, but the training of adversarial models can be notoriously unstable. Effectively balancing the performance of the generator and discriminator is critical, since a discriminator that achieves very high accuracy will produce relatively uninf... | VARIATIONAL DISCRIMINATOR BOTTLENECK: IMPROVING IMITATION LEARNING, INVERSE RL, AND GANS BY CONSTRAINING INFORMATION FLOW |
d252992508 | We consider robust clustering problems in R d , specifically k-clustering problems (e.g., k-Median and k-Means) with m outliers, where the cost for a given center set C ⊂ R d aggregates the distances from C to all but the furthest m data points, instead of all points as in classical clustering. We focus on the -coreset... | Near-optimal Coresets for Robust Clustering |
d10756563 | We study the problem of transferring a sample in one domain to an analog sample in another domain. Given two related domains, S and T , we would like to learn a generative function G that maps an input sample from S to the domain T , such that the output of a given function f , which accepts inputs in either domains, w... | UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION |
d260551862 | Adversarial training provides a means of regularizing supervised learning algorithms while virtual adversarial training is able to extend supervised learning algorithms to the semi-supervised setting. However, both methods require making small perturbations to numerous entries of the input vector, which is inappropriat... | ADVERSARIAL TRAINING METHODS FOR SEMI-SUPERVISED TEXT CLASSIFICATION |
d208139512 | Outlier detection and novelty detection are two important topics for anomaly detection. Suppose the majority of a dataset are drawn from a certain distribution, outlier detection and novelty detection both aim to detect data samples that do not fit the distribution. Outliers refer to data samples within this dataset, w... | ROBUST ANOMALY DETECTION AND BACKDOOR ATTACK DETECTION VIA DIFFERENTIAL PRIVACY |
d262217283 | Recurrent neural networks (RNNs) in the brain and in silico excel at solving tasks with intricate temporal dependencies.Long timescales required for solving such tasks can arise from properties of individual neurons (single-neuron timescale, τ , e.g., membrane time constant in biological neurons) or recurrent interacti... | Emergent mechanisms for long timescales depend on training curriculum and affect performance in memory tasks |
d235417295 | Generalization is one of the fundamental issues in machine learning. However, traditional techniques like uniform convergence may be unable to explain generalization under overparameterization (Nagarajan & Kolter, 2019). As alternative approaches, techniques based on stability analyze the training dynamics and derive a... | TOWARDS UNDERSTANDING GENERALIZATION VIA DECOMPOSING EXCESS RISK DYNAMICS |
d5834589 | The stochastic gradient descent (SGD) method and its variants are algorithms of choice for many Deep Learning tasks. These methods operate in a small-batch regime wherein a fraction of the training data, say 32-512 data points, is sampled to compute an approximation to the gradient. It has been observed in practice tha... | ON LARGE-BATCH TRAINING FOR DEEP LEARNING: GENERALIZATION GAP AND SHARP MINIMA |
d235613642 | Point cloud sequences are irregular and unordered in the spatial dimension while exhibiting regularities and order in the temporal dimension. Therefore, existing grid based convolutions for conventional video processing cannot be directly applied to spatio-temporal modeling of raw point cloud sequences. In this paper, ... | PSTNET: POINT SPATIO-TEMPORAL CONVOLUTION ON POINT CLOUD SEQUENCES |
d257365136 | Prompt tuning, in which a base pretrained model is adapted to each task via conditioning on learned prompt vectors, has emerged as a promising approach for efficiently adapting large language models to multiple downstream tasks. However, existing methods typically learn soft prompt vectors from scratch, and it has not ... | MULTITASK PROMPT TUNING ENABLES PARAMETER-EFFICIENT TRANSFER LEARNING |
d257427516 | Algorithmic case-based decision support provides examples to aid people in decision making tasks by providing contexts for a test case. Despite the promising performance of supervised learning, representations learned by supervised models may not align well with human intuitions: what models consider similar examples c... | LEARNING HUMAN-COMPATIBLE REPRESENTATIONS FOR CASE-BASED DECISION SUPPORT |
d3532296 | Successful recurrent models such as long short-term memories (LSTMs) and gated recurrent units (GRUs) use ad hoc gating mechanisms. Empirically these models have been found to improve the learning of medium to long term temporal dependencies and to help with vanishing gradient issues. We prove that learnable gates in a... | CAN RECURRENT NEURAL NETWORKS WARP TIME? |
d174801410 | Traditional set prediction models can struggle with simple datasets due to an issue we call the responsibility problem. We introduce a pooling method for sets of feature vectors based on sorting features across elements of the set. This can be used to construct a permutation-equivariant auto-encoder that avoids this re... | FSPOOL: LEARNING SET REPRESENTATIONS WITH FEATUREWISE SORT POOLING |
d201666520 | In this paper we consider self-supervised representation learning to improve sample efficiency in reinforcement learning (RL). We propose a forward prediction objective for simultaneously learning embeddings of states and action sequences. These embeddings capture the structure of the environment's dynamics, enabling e... | DYNAMICS-AWARE EMBEDDINGS |
d52908393 | Deep neuroevolution and deep reinforcement learning (deep RL) algorithms are two popular approaches to policy search. The former is widely applicable and rather stable, but suffers from low sample efficiency. By contrast, the latter is more sample efficient, but the most sample efficient variants are also rather unstab... | CEM-RL: Combining evolutionary and gradient-based methods for policy search |
d199577786 | Natural question generation (QG) is a challenging yet rewarding task, that aims to generate questions given an input passage and a target answer. Previous works on QG, however, either (i) ignore the rich structure information hidden in the word sequence, (ii) fail to fully exploit the target answer, or (iii) solely rel... | Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation |
d218595798 | In this paper we propose Flowtron: an autoregressive flow-based generative network for textto-speech synthesis with control over speech variation and style transfer. Flowtron borrows insights from IAF and revamps Tacotron in order to provide high-quality and expressive melspectrogram synthesis. Flowtron is optimized by... | Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis |
d235313572 | Vision Transformers (ViTs) and MLPs signal further efforts on replacing handwired features or inductive biases with general-purpose neural architectures. Existing works empower the models by massive data, such as large-scale pre-training and/or repeated strong data augmentations, and still report optimization-related p... | WHEN VISION TRANSFORMERS OUTPERFORM RESNETS WITHOUT PRE-TRAINING OR STRONG DATA AUGMENTATIONS |
d251647798 | Learning a single static convolutional kernel 1 in each convolutional layer is the common training paradigm of modern Convolutional Neural Networks (CNNs). Instead, recent research in dynamic convolution shows that learning a linear combination of n convolutional kernels weighted with their input-dependent attentions c... | OMNI-DIMENSIONAL DYNAMIC CONVOLUTION |
d52889459 | Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We ... | LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS |
d238419164 | What is the state of the art in continual machine learning? Although a natural question for predominant static benchmarks, the notion to train systems in a lifelong manner entails a plethora of additional challenges with respect to set-up and evaluation. The latter have recently sparked a growing amount of critiques on... | CLEVA-COMPASS: A CONTINUAL LEARNING EVALUATION ASSESSMENT COMPASS TO PROMOTE RESEARCH TRANSPARENCY AND COMPARABILITY |
d264935143 | While Large Language Models (LLMs) are increasingly being used in real-world applications, they remain vulnerable to prompt injection attacks: malicious third party prompts that subvert the intent of the system designer.To help researchers study this problem, we present a dataset of over 126,000 prompt injection attack... | TENSOR TRUST: INTERPRETABLE PROMPT INJECTION ATTACKS FROM AN ONLINE GAME |
d221136343 | Graph neural networks (GNNs) have demonstrated strong performance on a wide variety of tasks due to their ability to model non-uniform structured data. Despite their promise, there exists little research exploring methods to make these architectures more efficient at inference time. In this work, we explore the viabili... | Degree-Quant: Quantization-Aware Training for Graph Neural Networks |
d249152020 | Learning high-dimensional distributions is often done with explicit likelihood modeling or implicit modeling via minimizing integral probability metrics (IPMs). In this paper, we expand this learning paradigm to stochastic orders, namely, the convex or Choquet order between probability measures. Towards this end, explo... | Learning with Stochastic Orders |
d235390687 | In this work, we study the problem of clustering survival data -a challenging and so far under-explored task. We introduce a novel semi-supervised probabilistic approach to cluster survival data by leveraging recent advances in stochastic gradient variational inference. In contrast to previous work, our proposed method... | A DEEP VARIATIONAL APPROACH TO CLUSTERING SURVIVAL DATA |
d252438904 | We provide theoretical convergence guarantees for score-based generative models (SGMs) such as denoising diffusion probabilistic models (DDPMs), which constitute the backbone of large-scale realworld generative models such as DALL·E 2. Our main result is that, assuming accurate score estimates, such SGMs can efficientl... | Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions |
d29155623 | Network Embeddings (NEs) map the nodes of a given network into d-dimensional Euclidean space R d . Ideally, this mapping is such that 'similar' nodes are mapped onto nearby points, such that the NE can be used for purposes such as link prediction (if 'similar' means being 'more likely to be connected') or classificatio... | CONDITIONAL NETWORK EMBEDDINGS |
d49672236 | We study the problem of attacking a machine learning model in the hard-label black-box setting, where no model information is revealed except that the attacker can make queries to probe the corresponding hard-label decisions. This is a very challenging problem since the direct extension of state-of-the-art white-box at... | Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach |
d247595275 | Preference-based reinforcement learning (RL) has shown potential for teaching agents to perform the target tasks without a costly, pre-defined reward function by learning the reward with a supervisor's preference between the two agent behaviors. However, preference-based learning often requires a large amount of human ... | SURF: SEMI-SUPERVISED REWARD LEARNING WITH DATA AUGMENTATION FOR FEEDBACK-EFFICIENT PREFERENCE-BASED REINFORCEMENT LEARNING |
d215786510 | Many recent state-of-the-art methods for neural architecture search (NAS) relax the NAS problem into a joint continuous optimization over architecture parameters and their shared-weights, enabling the application of standard gradient-based optimizers. However, this training process remains poorly understood, as evidenc... | Geometry-Aware Gradient Algorithms for Neural Architecture Search |
d235485294 | In shuffle privacy, each user sends a collection of randomized messages to a trusted shuffler, the shuffler randomly permutes these messages, and the resulting shuffled collection of messages must satisfy differential privacy. Prior work in this model has largely focused on protocols that use a single round of communic... | SHUFFLE PRIVATE STOCHASTIC CONVEX OPTIMIZATION |
d263831292 | Learning representations that generalize to novel compositions of known concepts is crucial for bridging the gap between human and machine perception.One prominent effort is learning object-centric representations, which are widely conjectured to enable compositional generalization.Yet, it remains unclear when this con... | PROVABLE COMPOSITIONAL GENERALIZATION FOR OBJECT-CENTRIC LEARNING |
d246823979 | From the intuitive notion of disentanglement, the image variations corresponding to different factors should be distinct from each other, and the disentangled representation should reflect those variations with separate dimensions. To discover the factors and learn disentangled representation, previous methods typicall... | LEARNING DISENTANGLED REPRESENTATION BY EXPLOITING PRETRAINED GENERATIVE MODELS: A CONTRASTIVE LEARNING VIEW |
d238531782 | Goal-conditioned hierarchical reinforcement learning (GCHRL) provides a promising approach to solving long-horizon tasks. Recently, its success has been extended to more general settings by concurrently learning hierarchical policies and subgoal representations. Although GCHRL possesses superior exploration ability by ... | ACTIVE HIERARCHICAL EXPLORATION WITH STABLE SUBGOAL REPRESENTATION LEARNING |
d263831649 | Transfer learning is a crucial technique for handling a small amount of data that is potentially related to other abundant data. However, most of the existing methods are focused on classification tasks using images and language datasets. Therefore, in order to expand the transfer learning scheme to regression tasks, w... | Geometrically Aligned Transfer Encoder for Inductive Transfer in Regression Tasks |
d57721134 | We present an efficient convolution kernel for Convolutional Neural Networks (CNNs) on unstructured grids using parameterized differential operators while focusing on spherical signals such as panorama images or planetary signals. To this end, we replace conventional convolution kernels with linear combinations of diff... | SPHERICAL CNNS ON UNSTRUCTURED GRIDS |
d18114929 | Vector representations of words have heralded a transformational approach to classical problems in NLP; the most popular example is word2vec. However, a single vector does not suffice to model the polysemous nature of many (frequent) words, i.e., words with multiple meanings. In this paper, we propose a three-fold appr... | GEOMETRY OF POLYSEMY |
d252693198 | Predicting the binding structure of a small molecule ligand to a protein-a task known as molecular docking-is critical to drug design. Recent deep learning methods that treat docking as a regression problem have decreased runtime compared to traditional search-based methods but have yet to offer substantial improvement... | DIFFDOCK: DIFFUSION STEPS, TWISTS, AND TURNS FOR MOLECULAR DOCKING |
d256827051 | Synthesizing realistic animations of humans, animals, and even imaginary creatures, has long been a goal for artists and computer graphics professionals. Compared to the imaging domain, which is rich with large available datasets, the number of data instances for the motion domain is limited, particularly for the anima... | Single Motion Diffusion |
d259936734 | Scaling Transformers to longer sequence lengths has been a major problem in the last several years, promising to improve performance in language modeling and high-resolution image understanding, as well as to unlock new applications in code, audio, and video generation. The attention layer is the main bottleneck in sca... | FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning |
d247292764 | Predicting molecular conformations from molecular graphs is a fundamental problem in cheminformatics and drug discovery. Recently, significant progress has been achieved with machine learning approaches, especially with deep generative models. Inspired by the diffusion process in classical non-equilibrium thermodynamic... | GEODIFF: A GEOMETRIC DIFFUSION MODEL FOR MOLECULAR CONFORMATION GENERATION |
d203736530 | Recent research shows that the following two models are equivalent: (a) infinitely wide neural networks (NNs) trained under ℓ2 loss by gradient descent with infinitesimally small learning rate (b) kernel regression with respect to so-called Neural Tangent Kernels (NTKs)(Jacot et al., 2018). An efficient algorithm to co... | Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks |
d51979536 | Reinforcement learning algorithms rely on carefully engineering environment rewards that are extrinsic to the agent. However, annotating each environment with hand-designed, dense rewards is not scalable, motivating the need for developing reward functions that are intrinsic to the agent. Curiosity is a type of intrins... | Large-Scale Study of Curiosity-Driven Learning |
d235795764 | Source-free domain adaptation (SFDA) aims to adapt a model trained on labelled data in a source domain to unlabelled data in a target domain without access to the source-domain data during adaptation. Existing methods for SFDA leverage entropy-minimization techniques which: (i) apply only to classification; (ii) destro... | SOURCE-FREE ADAPTATION TO MEASUREMENT SHIFT VIA BOTTOM-UP FEATURE RESTORATION |
d225040049 | We study the problem of policy optimization for infinite-horizon discounted Markov Decision Processes with softmax policy and nonlinear function approximation trained with policy gradient algorithms. We concentrate on the training dynamics in the mean-field regime, modeling e.g., the behavior of wide single hidden laye... | Global optimality of softmax policy gradient with single hidden layer neural networks in the mean-field regime |
d11663659 | Probabilistic models with discrete latent variables naturally capture datasets composed of discrete classes. However, they are difficult to train efficiently, since backpropagation through discrete variables is generally not possible. We present a novel method to train a class of probabilistic models with discrete late... | DISCRETE VARIATIONAL AUTOENCODERS |
d259298536 | The monotonic dependence of the outputs of a neural network on some of its inputs is a crucial inductive bias in many scenarios where domain knowledge dictates such behavior.This is especially important for interpretability and fairness considerations.In a broader context, scenarios in which monotonicity is important c... | EXPRESSIVE MONOTONIC NEURAL NETWORKS |
d259108646 | Image recognition and generation have long been developed independently of each other. With the recent trend towards general-purpose representation learning, the development of general representations for both recognition and generation tasks is also promoted. However, preliminary attempts mainly focus on generation pe... | ADDP: Learning General Representations for Image Recognition and Generation with Alternating Denoising Diffusion Process |
d202749904 | Generative adversarial networks have seen rapid development in recent years and have led to remarkable improvements in generative modelling of images. However, their application in the audio domain has received limited attention, and autoregressive models, such as WaveNet, remain the state of the art in generative mode... | HIGH FIDELITY SPEECH SYNTHESIS WITH ADVERSARIAL NETWORKS |
d247011606 | Recent success in cooperative multi-agent reinforcement learning (MARL) relies on centralized training and policy sharing. Centralized training eliminates the issue of non-stationarity MARL yet induces large communication costs, and policy sharing is empirically crucial to efficient learning in certain tasks yet lacks ... | COMMUNICATION-EFFICIENT ACTOR-CRITIC METHODS FOR HOMOGENEOUS MARKOV GAMES |
d203836132 | When humans observe a physical system, they can easily locate objects, understand their interactions, and anticipate future behavior. For computers, however, learning such models from videos in an unsupervised fashion is an unsolved research problem. In this paper, we present STOVE, a novel state-space model for videos... | STRUCTURED OBJECT-AWARE PHYSICS PREDICTION FOR VIDEO MODELING AND PLANNING |
d245837784 | Federated learning is a popular technology for training machine learning models on distributed data sources without sharing data. Vertical federated learning or feature-based federated learning applies to the cases that different data sources share the same sample ID space but differ in feature space. To ensure the dat... | Fair and efficient contribution valuation for vertical federated learning |
d257353252 | Deep learning has had tremendous success at learning low-dimensional representations of high-dimensional data. This success would be impossible if there was no hidden low-dimensional structure in data of interest; this existence is posited by the manifold hypothesis, which states that the data lies on an unknown manifo... | VERIFYING THE UNION OF MANIFOLDS HYPOTHESIS FOR IMAGE DATA |
d240288595 | In this paper, we propose a novel Collaborative Pure Exploration in Kernel Bandit model (CoPE-KB), where multiple agents collaborate to complete different but related tasks with limited communication. Our model generalizes prior CoPE formulation with the single-task and classic MAB setting to allow multiple tasks and g... | COLLABORATIVE PURE EXPLORATION IN KERNEL BANDIT |
d246634117 | Catastrophic forgetting is one of the major challenges in continual learning. To address this issue, some existing methods put restrictive constraints on the optimization space of the new task for minimizing the interference to old tasks. However, this may lead to unsatisfactory performance for the new task, especially... | TRGP: TRUST REGION GRADIENT PROJECTION FOR CONTINUAL LEARNING |
d3485693 | We consider the problem of learning a one-hidden-layer neural network: we assume the input x ∈ R d is from Gaussian distribution and the label y = a ⊤ σ(Bx) + ξ, where a is a nonnegative vector in R m with m ≤ d, B ∈ R m×d is a full-rank weight matrix, and ξ is a noise vector. We first give an analytic formula for the ... | Learning One-hidden-layer Neural Networks with Landscape Design |
d204785044 | Equivariance is a nice property to have as it produces much more parameter efficient neural architectures and preserves the structure of the input through the feature mapping. Even though some combinations of transformations might never appear (e.g., an upright face with a horizontal nose), current equivariant architec... | CO-ATTENTIVE EQUIVARIANT NEURAL NETWORKS: FOCUSING EQUIVARIANCE ON TRANSFORMATIONS CO-OCCURRING IN DATA |
d260203143 | We introduce new jailbreak attacks on vision language models (VLMs), which use aligned LLMs and are resilient to text-only jailbreak attacks.Specifically, we develop cross-modality attacks on alignment where we pair adversarial images going through the vision encoder with textual prompts to break the alignment of the l... | JAILBREAK IN PIECES: COMPOSITIONAL ADVERSARIAL ATTACKS ON MULTI-MODAL LANGUAGE MODELS |
d257280083 | Unsupervised meta-learning aims to learn generalizable knowledge across a distribution of tasks constructed from unlabeled data. Here, the main challenge is how to construct diverse tasks for meta-learning without label information; recent works have proposed to create, e.g., pseudo-labeling via pretrained representati... | UNSUPERVISED META-LEARNING VIA FEW-SHOT PSEUDO-SUPERVISED CONTRASTIVE LEARNING |
d253224274 | We introduce Noise Injection Node Regularization (NINR), a method of injecting structured noise into Deep Neural Networks (DNN) during the training stage, resulting in an emergent regularizing effect.We present theoretical and empirical evidence for substantial improvement in robustness against various test data pertur... | NOISE INJECTION NODE REGULARIZATION FOR ROBUST LEARNING |
d208548538 | Formal verification of neural networks is essential for their deployment in safetycritical areas. Many available formal verification methods have been shown to be instances of a unified Branch and Bound (BaB) formulation. We propose a novel framework for designing an effective branching strategy for BaB. Specifically, ... | NEURAL NETWORK BRANCHING FOR NEURAL NETWORK VERIFICATION |
d59310641 | Self-attention is a useful mechanism to build generative models for language and images. It determines the importance of context elements by comparing each element to the current time step. In this paper, we show that a very lightweight convolution can perform competitively to the best reported self-attention results. ... | PAY LESS ATTENTION WITH LIGHTWEIGHT AND DYNAMIC CONVOLUTIONS |
d252762544 | Dynamic benchmarks interweave model fitting and data collection in an attempt to mitigate the limitations of static benchmarks. In contrast to an extensive theoretical and empirical study of the static setting, the dynamic counterpart lags behind due to limited empirical studies and no apparent theoretical foundation t... | A THEORY OF DYNAMIC BENCHMARKS |
d263829270 | Learning a precise dynamics model can be crucial for offline reinforcement learning, which, unfortunately, has been found to be quite challenging.Dynamics models that are learned by fitting historical transitions often struggle to generalize to unseen transitions.In this study, we identify a hidden but pivotal factor t... | REWARD-CONSISTENT DYNAMICS MODELS ARE STRONGLY GENERALIZABLE FOR OFFLINE REINFORCEMENT LEARNING |
d53104061 | Weight decay is one of the standard tricks in the neural network toolbox, but the reasons for its regularization effect are poorly understood, and recent results have cast doubt on the traditional interpretation in terms of L 2 regularization. Literal weight decay has been shown to outperform L 2 regularization for opt... | THREE MECHANISMS OF WEIGHT DECAY REGULARIZATION |
d219558527 | We study the problem of few-sample fine-tuning of BERT contextual representations, and identify three sub-optimal choices in current, broadly adopted practices. First, we observe that the omission of the gradient bias correction in the BERTADAM optimizer results in fine-tuning instability. We also find that parts of th... | Revisiting Few-sample BERT Fine-tuning |
d225039786 | Offline reinforcement learning seeks to utilize offline (observational) data to guide the learning of (causal) sequential decision making strategies. The hope is that offline reinforcement learning coupled with function approximation methods (to deal with the curse of dimensionality) can provide a means to help allevia... | What are the Statistical Limits of Offline RL with Linear Function Approximation? |
d259847777 | Generative adversarial networks (GANs) learn a target probability distribution by optimizing a generator and a discriminator with minimax objectives. This paper addresses the question of whether such optimization actually provides the generator with gradients that make its distribution close to the target distribution.... | SAN: INDUCING METRIZABILITY OF GAN WITH DISCRIMINATIVE NORMALIZED LINEAR LAYER |
d239016615 | Despite their success and popularity, deep neural networks (DNNs) are vulnerable when facing backdoor attacks. This impedes their wider adoption, especially in mission critical applications. This paper tackles the problem of Trojan detection, namely, identifying Trojaned models -models trained with poisoned data. One p... | TRIGGER HUNTING WITH A TOPOLOGICAL PRIOR FOR TROJAN DETECTION |
d238419227 | We study the memorization power of feedforward ReLU neural networks. We show that such networks can memorize any N points that satisfy a mild separability assumption usingÕ √ N parameters. Known VC-dimension upper bounds imply that memorizing N samples requires Ω( √ N ) parameters, and hence our construction is optimal... | On the Optimal Memorization Power of ReLU Neural Networks |
d237491529 | Discrete-continuous hybrid action space is a natural setting in many practical problems, such as robot control and game AI. However, most previous Reinforcement Learning (RL) works only demonstrate the success in controlling with either discrete or continuous action space, while seldom take into account the hybrid acti... | HYAR: ADDRESSING DISCRETE-CONTINUOUS ACTION REINFORCEMENT LEARNING VIA HYBRID ACTION REPRESENTATION |
d262825257 | Given a sample of size N , it is often useful to select a subsample of smaller size n < N to be used for statistical estimation or learning. Such a data selection step is useful to reduce the requirements of data labeling and the computational complexity of learning. We assume to be given N unlabeled samples {x i } i≤N... | Towards a statistical theory of data selection under weak supervision |
d259298630 | Overconfidence is a common issue for deep neural networks, limiting their deployment in real-world applications. To better estimate confidence, existing methods mostly focus on fully-supervised scenarios and rely on training labels. In this paper, we propose the first confidence estimation method for a semi-supervised ... | CONFIDENCE ESTIMATION USING UNLABELED DATA |
d2971655 | Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent's parameters, which can lead to more consistent exploration and a richer set of behaviors. Methods such as evolutionary strategies use param... | Parameter Space Noise for Exploration |
d221655767 | Visual cognition of primates is superior to that of artificial neural networks in its ability to "envision" a visual object, even a newly-introduced one, in different attributes including pose, position, color, texture, etc. To aid neural networks to envision objects with different attributes, we propose a family of ob... | Zero-shot Synthesis with Group-Supervised Learning |
d251622295 | Multi-objective reinforcement learning (MORL) approaches have emerged to tackle many real-world problems with multiple conflicting objectives by maximizing a joint objective function weighted by a preference vector. These approaches find fixed customized policies corresponding to preference vectors specified during tra... | PD-MORL: PREFERENCE-DRIVEN MULTI-OBJECTIVE REINFORCEMENT LEARNING ALGORITHM |
d108297336 | Presently the most successful approaches to semi-supervised learning are based on consistency regularization, whereby a model is trained to be robust to small perturbations of its inputs and parameters. To understand consistency regularization, we conceptually explore how loss geometry interacts with training procedure... | THERE ARE MANY CONSISTENT EXPLANATIONS OF UNLABELED DATA: WHY YOU SHOULD AVERAGE |
d3461223 | Domain adaptation refers to the problem of leveraging labeled data in a source domain to learn an accurate model in a target domain where labels are scarce or unavailable. A recent approach for finding a common representation of the two domains is via domain adversarial training(Ganin & Lempitsky, 2015), which attempts... | A DIRT-T APPROACH TO UNSUPERVISED DOMAIN ADAPTATION |
d203736888 | We present a novel generative model that combines state-of-the-art neural text-tospeech (TTS) with semi-supervised probabilistic latent variable models. By providing partial supervision to some of the latent variables, we are able to force them to take on consistent and interpretable purposes, which previously hasn't b... | SEMI-SUPERVISED GENERATIVE MODELING FOR CONTROLLABLE SPEECH SYNTHESIS |
d264426013 | In distributed deep learning with data parallelism, synchronizing gradients at each training step can cause a huge communication overhead, especially when many nodes work together to train large models.Local gradient methods, such as Local SGD, address this issue by allowing workers to compute locally for H steps witho... | A QUADRATIC SYNCHRONIZATION RULE FOR DISTRIBUTED DEEP LEARNING |
d268041608 | Accurate blind docking has the potential to lead to new biological breakthroughs, but for this promise to be realized, docking methods must generalize well across the proteome.Existing benchmarks, however, fail to rigorously assess generalizability.Therefore, we develop DOCKGEN, a new benchmark based on the ligandbindi... | DEEP CONFIDENT STEPS TO NEW POCKETS: STRATEGIES FOR DOCKING GENERALIZATION |
d257404922 | Premise selection is a fundamental problem of automated theorem proving. Previous works often use intricate symbolic methods, rely on domain knowledge, and require significant engineering effort to solve this task. In this work, we show that Magnushammer, a neural transformer-based approach, can outperform traditional ... | Magnushammer: A Transformer-based Approach to Premise Selection |
d263792492 | Text-guided 3D shape generation remains challenging due to the absence of large paired text-shape dataset, the substantial semantic gap between these two modalities, and the structural complexity of 3D shapes. This paper presents a new framework called Image as Stepping Stone (ISS) for the task by introducing 2D image ... | ISS: IMAGE AS STEPPING STONE FOR TEXT-GUIDED 3D SHAPE GENERATION |
d247292326 | Federated learning (FL) enables distributed optimization of machine learning models while protecting privacy by independently training local models on each client and then aggregating parameters on a central server, thereby producing an effective global model. Although a variety of FL algorithms have been proposed, the... | ACCELERATION OF FEDERATED LEARNING WITH ALLEVIATED FORGETTING IN LOCAL TRAINING |
d15534684 | Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification. For instance, a good representation for 2D images might be one that describes only global structure and discards information about detailed texture. In this paper... | VARIATIONAL LOSSY AUTOENCODER |
d245329426 | Reinforcement learning (RL) provides a naturalistic framing for learning through trial and error, which is appealing both because of its simplicity and effectiveness and because of its resemblance to how humans and animals acquire skills through experience. However, real-world embodied learning, such as that performed ... | AUTONOMOUS REINFORCEMENT LEARNING: FORMALISM AND BENCHMARKING |
d222134104 | Natural data are often long-tail distributed over semantic classes. Existing recognition methods tackle this imbalanced classification by placing more emphasis on the tail data, through class re-balancing/re-weighting or ensembling over different data groups, resulting in increased tail accuracies but reduced head accu... | LONG-TAILED RECOGNITION BY ROUTING DIVERSE DISTRIBUTION-AWARE EXPERTS |
d219721074 | Despite their popularity, to date, the application of normalizing flows on categorical data stays limited. The current practice of using dequantization to map discrete data to a continuous space is inapplicable as categorical data has no intrinsic order. Instead, categorical data have complex and latent relations that ... | Categorical Normalizing Flows via Continuous Transformations |
d235613568 | Federated learning aims to collaboratively train a strong global model by accessing users' locally trained models but not their own data. A crucial step is therefore to aggregate local models into a global model, which has been shown challenging when users have non-i.i.d. data. In this paper, we propose a novel aggrega... | FEDBE: MAKING BAYESIAN MODEL ENSEMBLE APPLICABLE TO FEDERATED LEARNING |
d210843644 | Message-passing neural networks (MPNNs) have been successfully applied to representation learning on graphs in a variety of real-world applications. However, two fundamental weaknesses of MPNNs' aggregators limit their ability to represent graph-structured data: losing the structural information of nodes in neighborhoo... | GEOM-GCN: GEOMETRIC GRAPH CONVOLUTIONAL NETWORKS |
d227126808 | In this work, we examine the security of InstaHide, a scheme recently proposed by [Huang, Song, Li and Arora, ICML'20] for preserving the security of private datasets in the context of distributed learning. To generate a synthetic training example to be shared among the distributed learners, InstaHide takes a convex ... | On InstaHide, Phase Retrieval, and Sparse Matrix Factorization |
d49667762 | Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train. Feed-forward and convolutional architectures have recently been show... | UNIVERSAL TRANSFORMERS |
d259308864 | We study the problem of watermarking large language models (LLMs) generated text -one of the most promising approaches for addressing the safety challenges of LLM usage. In this paper, we propose a rigorous theoretical framework to quantify the effectiveness and robustness of LLM watermarks. We propose a robust and hig... | Provable Robust Watermarking for AI-Generated Text |
d44084312 | A We characterize the singular values of the linear transformation associated with a standard 2D multi-channel convolutional layer, enabling their efficient computation. This characterization also leads to an algorithm for projecting a convolutional layer onto an operator-norm ball. We show that this is an effective re... | The Singular Values of Convolutional Layers |
d262411072 | We present an algorithm for policy search in stochastic dynamical systems using model-based reinforcement learning.The system dynamics are described with Bayesian neural networks (BNNs) that include stochastic input variables.These input variables allow us to capture complex statistical patterns in the transition dynam... | LEARNING AND POLICY SEARCH IN STOCHASTIC DYNAMICAL SYSTEMS WITH BAYESIAN NEURAL NETWORKS |
d220249831 | Real-world large-scale datasets are heteroskedastic and imbalanced -labels have varying levels of uncertainty and label distributions are long-tailed. Heteroskedasticity and imbalance challenge deep learning algorithms due to the difficulty of distinguishing among mislabeled, ambiguous, and rare examples. Addressing he... | Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization |
d244130255 | Despite the recent success of Graph Neural Networks (GNNs), training GNNs on large graphs remains challenging. The limited resource capacities of the existing servers, the dependency between nodes in a graph, and the privacy concern due to the centralized storage and model learning have spurred the need to design an ef... | LEARN LOCALLY, CORRECT GLOBALLY: A DISTRIBUTED ALGORITHM FOR TRAINING GRAPH NEURAL NETWORKS |
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