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d3693334 | Neural network training relies on our ability to find "good" minimizers of highly non-convex loss functions. It is well-known that certain network architecture designs (e.g., skip connections) produce loss functions that train easier, and wellchosen training parameters (batch size, learning rate, optimizer) produce min... | Visualizing the Loss Landscape of Neural Nets |
d251903874 | Dynamical systems with interacting agents are universal in nature, commonly modeled by a graph of relationships between their constituents. Recently, various works have been presented to tackle the problem of inferring those relationships from the system trajectories via deep neural networks, but most of the studies as... | Learning Heterogeneous Interaction Strengths by Trajectory Prediction with Graph Neural Network |
d3144218 | We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions.... | SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS |
d218487073 | Generative adversarial networks are the state of the art for generative modeling in vision, yet are notoriously unstable in practice. This instability is further exacerbated with limited training data. However, in the synthesis of domains such as medical or satellite imaging, it is often overlooked that the image label... | Group Equivariant Generative Adversarial Networks |
d3421136 | We offer a generalized point of view on the backpropagation algorithm, currently the most common technique to train neural networks via stochastic gradient descent and variants thereof. Specifically, we show that backpropagation of a prediction error is equivalent to sequential gradient descent steps on a quadratic pen... | Proximal Backpropagation |
d203838325 | We study the following three fundamental problems about ridge regression: (1) what is the structure of the estimator? (2) how to correctly use cross-validation to choose the regularization parameter? and (3) how to accelerate computation without losing too much accuracy? We consider the three problems in a unified larg... | Ridge Regression: Structure, Cross-Validation, and Sketching |
d52922125 | A central goal of unsupervised learning is to acquire representations from unlabeled data or experience that can be used for more effective learning of downstream tasks from modest amounts of labeled data. Many prior unsupervised learning works aim to do so by developing proxy objectives based on reconstruction, disent... | UNSUPERVISED LEARNING VIA META-LEARNING |
d7782433 | Exploration in complex domains is a key challenge in reinforcement learning, especially for tasks with very sparse rewards. Recent successes in deep reinforcement learning have been achieved mostly using simple heuristic exploration strategies such as -greedy action selection or Gaussian control noise, but there are ma... | SURPRISE-BASED INTRINSIC MOTIVATION FOR DEEP REINFORCEMENT LEARNING |
d3480671 | Existing approaches to neural machine translation condition each output word on previously generated outputs. We introduce a model that avoids this autoregressive property and produces its outputs in parallel, allowing an order of magnitude lower latency during inference. Through knowledge distillation, the use of inpu... | NON-AUTOREGRESSIVE NEURAL MACHINE TRANSLATION |
d186206649 | Generative adversarial networks have been very successful in generative modeling, however they remain relatively hard to optimize compared to standard deep neural networks. In this paper, we try to gain insight into the optimization of GANs by looking at the game vector field resulting from the concatenation of the gra... | A Closer Look at the Optimization Landscapes of Generative Adversarial Networks |
d238634411 | Implicit deep learning has received increasing attention recently, since it generalizes the recursive prediction rules of many commonly used neural network architectures. Its prediction rule is provided implicitly based on the solution of an equilibrium equation. Although many recent studies have experimentally demonst... | A GLOBAL CONVERGENCE THEORY FOR DEEP RELU IMPLICIT NETWORKS VIA OVER-PARAMETERIZATION |
d263672060 | Diffusion models have demonstrated remarkable capabilities in image synthesis and related generative tasks.Nevertheless, their practicality for low-latency real-world applications is constrained by substantial computational costs and latency issues.Quantization is a dominant way to compress and accelerate diffusion mod... | EFFICIENTDM: EFFICIENT QUANTIZATION-AWARE FINE-TUNING OF LOW-BIT DIFFUSION MODELS |
d7443908 | Recurrent neural networks have been very successful at predicting sequences of words in tasks such as language modeling. However, all such models are based on the conventional classification framework, where the model is trained against one-hot targets, and each word is represented both as an input and as an output in ... | TYING WORD VECTORS AND WORD CLASSIFIERS: A LOSS FRAMEWORK FOR LANGUAGE MODELING |
d220249726 | Transformer architectures have proven to learn useful representations for protein classification and generation tasks. However, these representations present challenges in interpretability. Through the lens of attention, we analyze the inner workings of the Transformer and explore how the model discerns structural and ... | BERTology Meets Biology: Interpreting Attention in Protein Language Models |
d233219849 | When answering complex questions, people can seamlessly combine information from visual, textual and tabular sources. While interest in models that reason over multiple pieces of evidence has surged in recent years, there has been relatively little work on question answering models that reason across multiple modalitie... | MULTIMODALQA: COMPLEX QUESTION ANSWERING OVER TEXT, TABLES AND IMAGES |
d258480014 | Figure 1: We propose Texture UV Radiance Fields (TUVF) to render a 3D consistent texture given a 3D object shape input.TUVF provides a category-level texture representation disentangled from 3D shapes.Top three rows: TUVF can synthesize realistic textures by training from a collection of single-view images; Fourth row:... | TUVF: LEARNING GENERALIZABLE TEXTURE UV RADIANCE FIELDS |
d258832501 | Large language models (LLMs) demonstrate remarkable medical expertise, but data privacy concerns impede their direct use in healthcare environments. Although offering improved data privacy protection, domain-specific small language models (SLMs) often underperform LLMs, emphasizing the need for methods that reduce this... | Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting |
d225076232 | Deep learning is slowly, but steadily, hitting a memory bottleneck. While the tensor computation in top-of-the-line GPUs increased by 32× over the last five years, the total available memory only grew by 2.5×. This prevents researchers from exploring larger architectures, as training large networks requires more memory... | MEMORY OPTIMIZATION FOR DEEP NETWORKS |
d263620670 | Markov Decision Processes (MDPs) are a formal framework for modeling and solving sequential decision-making problems. In finite-time horizons such problems are relevant for instance for optimal stopping or specific supply chain problems, but also in the training of large language models. In contrast to infinite horizon... | Beyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods |
d259164846 | In few-shot recognition, a classifier that has been trained on one set of classes is required to rapidly adapt and generalize to a disjoint, novel set of classes. To that end, recent studies have shown the efficacy of fine-tuning with carefully crafted adaptation architectures. However this raises the question of: How ... | Neural Fine-Tuning Search for Few-Shot Learning |
d46899514 | Generative models for source code are an interesting structured prediction problem, requiring to reason about both hard syntactic and semantic constraints as well as about natural, likely programs. We present a novel model for this problem that uses a graph to represent the intermediate state of the generated output. T... | Generative Code Modeling with Graphs |
d255340454 | State space models (SSMs) have demonstrated state-of-the-art sequence modeling performance in some modalities, but underperform attention in language modeling. Moreover, despite scaling nearly linearly in sequence length instead of quadratically, SSMs are still slower than Transformers due to poor hardware utilization.... | Hungry Hungry Hippos: Towards Language Modeling with State Space Models |
d208229926 | Masked language modeling (MLM) pre-training methods such as BERT corrupt the input by replacing some tokens with [MASK] and then train a model to reconstruct the original tokens. While they produce good results when transferred to downstream NLP tasks, they generally require large amounts of compute to be effective. A... | ELECTRA: PRE-TRAINING TEXT ENCODERS AS DISCRIMINATORS RATHER THAN GENERATORS |
d258427077 | The goal of multi-objective reinforcement learning (MORL) is to learn policies that simultaneously optimize multiple competing objectives. In practice, an agent's preferences over the objectives may not be known apriori, and hence, we require policies that can generalize to arbitrary preferences at test time. In this w... | SCALING PARETO-EFFICIENT DECISION MAKING VIA OFFLINE MULTI-OBJECTIVE RL |
d263134283 | Transformers have recently emerged as a powerful tool for learning visual representations.In this paper, we identify and characterize artifacts in feature maps of both supervised and self-supervised ViT networks.The artifacts correspond to high-norm tokens appearing during inference primarily in low-informative backgro... | VISION TRANSFORMERS NEED REGISTERS |
d8968704 | We present sketch-rnn, a recurrent neural network (RNN) able to construct stroke-based drawings of common objects. The model is trained on a dataset of human-drawn images representing many different classes. We outline a framework for conditional and unconditional sketch generation, and describe new robust training met... | A Neural Representation of Sketch Drawings |
d263310452 | Large training sets have become a cornerstone of machine learning and are the foundation for recent advances in language modeling and multimodal learning. While data curation for pre-training is often still ad-hoc, one common paradigm is to first collect a massive pool of data from the Web and then filter this candidat... | Data Filtering Networks |
d44129557 | Variational autoencoders (VAEs) have shown a promise in data-driven conversation modeling. However, most VAE conversation models match the approximate posterior distribution over the latent variables to a simple prior such as standard normal distribution, thereby restricting the generated responses to a relatively simp... | DIALOGWAE: MULTIMODAL RESPONSE GENERATION WITH CONDITIONAL WASSERSTEIN AUTO-ENCODER |
d259108266 | Instruction tuning large language models (LLMs) remains a challenging task, owing to the complexity of hyperparameter selection and the difficulty involved in evaluating the tuned models. To determine the optimal hyperparameters, an automatic, robust, and reliable evaluation benchmark is essential. However, establishin... | PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization |
d231955136 | A deep equilibrium model uses implicit layers, which are implicitly defined through an equilibrium point of an infinite sequence of computation. It avoids any explicit computation of the infinite sequence by finding an equilibrium point directly via root-finding and by computing gradients via implicit differentiation.I... | ON THE THEORY OF IMPLICIT DEEP LEARNING: GLOBAL CONVERGENCE WITH IMPLICIT LAYERS |
d246633939 | Existing offline reinforcement learning (RL) methods face a few major challenges, particularly the distributional shift between the learned policy and the behavior policy. Offline Meta-RL is emerging as a promising approach to address these challenges, aiming to learn an informative meta-policy from a collection of tas... | MODEL-BASED OFFLINE META-REINFORCEMENT LEARNING WITH REGULARIZATION |
d259287121 | The ever-growing complexity of reinforcement learning (RL) tasks demands a distributed RL system to efficiently generate and process a massive amount of data to train intelligent agents. However, existing open-source libraries suffer from various limitations, which impede their practical use in challenging scenarios wh... | SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores |
d108306764 | Langevin diffusion is a powerful tool for nonconvex optimization problems, which can be used to find the global minima. However, the standard Langevin diffusion driven by a single temperature suffers from the tradeoff between "global exploration" and "local exploitation", corresponding the high and low temperatures, re... | Accelerating Nonconvex Learning via Replica Exchange Langevin Diffusion |
d261557333 | Equilibrium propagation (EP) is a compelling alternative to the backpropagation of error algorithm (BP) for computing gradients of neural networks on biological or analog neuromorphic substrates. Still, the algorithm requires weight symmetry and infinitesimal equilibrium perturbations, i.e., nudges, to estimate unbiase... | Improving equilibrium propagation without weight symmetry through Jacobian homeostasis |
d247292561 | We present DINO (DETR with Improved deNoising anchOr boxes), a state-of-the-art end-to-end object detector. DINO improves over previous DETR-like models in performance and efficiency by using a contrastive way for denoising training, a mixed query selection method for anchor initialization, and a look forward twice sch... | DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection |
d85469987 | We analyze the expressiveness and loss surface of practical deep convolutional neural networks (CNNs) with shared weights and max pooling layers. We show that such CNNs produce linearly independent features at a "wide" layer which has more neurons than the number of training samples. This condition holds e.g. for the V... | THE LOSS SURFACE AND EXPRESSIVITY OF DEEP CONVOLUTIONAL NEURAL NETWORKS |
d250491294 | As a long-term threat to the privacy of training data, membership inference attacks (MIAs) emerge ubiquitously in machine learning models. Existing works evidence strong connection between the distinguishability of the training and testing loss distributions and the model's vulnerability to MIAs. Motivated by existing ... | RELAXLOSS: DEFENDING MEMBERSHIP INFERENCE ATTACKS WITHOUT LOSING UTILITY |
d253157500 | Sim-to-real transfer trains RL agents in the simulated environments and then deploys them in the real world. Sim-to-real transfer has been widely used in practice because it is often cheaper, safer and much faster to collect samples in simulation than in the real world. Despite the empirical success of the sim-to-real ... | Provable Sim-to-real Transfer in Continuous Domain with Partial Observations |
d246210223 | Localizing keypoints of an object is a basic visual problem. However, supervised learning of a keypoint localization network often requires a large amount of data, which is expensive and time-consuming to obtain. To remedy this, there is an ever-growing interest in semi-supervised learning (SSL), which leverages a smal... | PSEUDO-LABELED AUTO-CURRICULUM LEARNING FOR SEMI-SUPERVISED KEYPOINT LOCALIZATION |
d261437068 | Recurrent Neural Networks (RNN) are widely used to solve a variety of problems and as the quantity of data and the amount of available compute have increased, so have model sizes. The number of parameters in recent state-of-the-art networks makes them hard to deploy, especially on mobile phones and embedded devices. Th... | EXPLORING SPARSITY IN RECURRENT NEURAL NETWORKS |
d204575791 | This paper presents a phenomenon in neural networks that we refer to as local elasticity. Roughly speaking, a classifier is said to be locally elastic if its prediction at a feature vector x is not significantly perturbed, after the classifier is updated via stochastic gradient descent at a (labeled) feature vector x t... | THE LOCAL ELASTICITY OF NEURAL NETWORKS |
d53022741 | This paper concerns dictionary learning, i.e., sparse coding, a fundamental representation learning problem. We show that a subgradient descent algorithm, with random initialization, can provably recover orthogonal dictionaries on a natural nonsmooth, nonconvex 1 minimization formulation of the problem, under mild stat... | Subgradient Descent Learns Orthogonal Dictionaries |
d247594476 | 3D shape analysis has been widely explored in the era of deep learning. Numerous models have been developed for various 3D data representation formats, e.g., MeshCNN for meshes, PointNet for point clouds and VoxNet for voxels. In this study, we present Representation-Agnostic Shape Fields (RASF), a generalizable and co... | REPRESENTATION-AGNOSTIC SHAPE FIELDS |
d252917981 | Large language models (LLMs) show impressive abilities via few-shot prompting. Commercialized APIs such as OpenAI GPT-3 further increase their use in real-world language applications. However, the crucial problem of how to improve the reliability of GPT-3 is still under-explored. While reliability is a broad and vaguel... | PROMPTING GPT-3 TO BE RELIABLE |
d256827373 | Class imbalance problems frequently occur in real-world tasks, and conventional deep learning algorithms are well known for performance degradation on imbalanced training datasets. To mitigate this problem, many approaches have aimed to balance among given classes by re-weighting or re-sampling training samples. These ... | CUDA: CURRICULUM OF DATA AUGMENTATION FOR LONG-TAILED RECOGNITION |
d108296106 | Distributed optimization is vital in solving large-scale machine learning problems. A widely-shared feature of distributed optimization techniques is the requirement that all nodes complete their assigned tasks in each computational epoch before the system can proceed to the next epoch. In such settings, slow nodes, ca... | ANYTIME MINIBATCH: EXPLOITING STRAGGLERS IN ONLINE DISTRIBUTED OPTIMIZATION |
d238583615 | Solving the Schrödinger equation is key to many quantum mechanical properties. However, an analytical solution is only tractable for single-electron systems. Recently, neural networks succeeded at modeling wave functions of many-electron systems. Together with the variational Monte-Carlo (VMC) framework, this led to so... | AB-INITIO POTENTIAL ENERGY SURFACES BY PAIRING GNNS WITH NEURAL WAVE FUNCTIONS |
d53405705 | Ordinary stochastic neural networks mostly rely on the expected values of their weights to make predictions, whereas the induced noise is mostly used to capture the uncertainty, prevent overfitting and slightly boost the performance through test-time averaging. In this paper, we introduce variance layers, a different k... | VARIANCE NETWORKS: WHEN EXPECTATION DOES NOT MEET YOUR EXPECTATIONS |
d222140724 | Learning functions on point clouds has applications in many fields, including computer vision, computer graphics, physics, and chemistry. Recently, there has been a growing interest in neural architectures that are invariant or equivariant to all three shape-preserving transformations of point clouds: translation, rota... | ON THE UNIVERSALITY OF ROTATION EQUIVARIANT POINT CLOUD NETWORKS |
d254636202 | Pretrained large-scale vision-language models like CLIP have exhibited strong generalization over unseen tasks. Yet imperceptible adversarial perturbations can significantly reduce CLIP's performance on new tasks. In this work, we identify and explore the problem of adapting large-scale models for zero-shot adversarial... | UNDERSTANDING ZERO-SHOT ADVERSARIAL ROBUSTNESS FOR LARGE-SCALE MODELS |
d263830334 | Differentially private learning algorithms inject noise into the learning process. While the most common private learning algorithm, DP-SGD, adds independent Gaussian noise in each iteration, recent work on matrix factorization mechanisms has shown empirically that introducing correlations in the noise can greatly impr... | Correlated Noise Provably Beats Independent Noise for Differentially Private Learning |
d213179475 | Almost all current adversarial attacks of CNN classifiers rely on information derived from the output layer of the network. This work presents a new adversarial attack based on the modeling and exploitation of class-wise and layer-wise deep feature distributions. We achieve state-of-the-art targeted blackbox transfer-b... | TRANSFERABLE PERTURBATIONS OF DEEP FEATURE DISTRIBUTIONS |
d10413462 | We study the behavior of untrained neural networks whose weights and biases are randomly distributed using mean field theory. We show the existence of depth scales that naturally limit the maximum depth of signal propagation through these random networks. Our main practical result is to show that random networks may be... | DEEP INFORMATION PROPAGATION |
d220128067 | Sequence-to-sequence (seq2seq) learners are widely used, but we still have only limited knowledge about what inductive biases shape the way they generalize. We address that by investigating how popular seq2seq learners generalize in tasks that have high ambiguity in the training data. We use SCAN and three new tasks to... | What they do when in doubt: a study of inductive biases in seq2seq learners |
d211092198 | Responding with knowledge has been recognized as an important capability for an intelligent conversational agent. Yet knowledge-grounded dialogues, as training data for learning such a response generation model, are difficult to obtain. Motivated by the challenge in practice, we consider knowledge-grounded dialogue gen... | LOW-RESOURCE KNOWLEDGE-GROUNDED DIALOGUE GENERATION |
d231662084 | Mel-filterbanks are fixed, engineered audio features which emulate human perception and have been used through the history of audio understanding up to today. However, their undeniable qualities are counterbalanced by the fundamental limitations of handmade representations. In this work we show that we can train a sing... | LEAF: A LEARNABLE FRONTEND FOR AUDIO CLASSIFICATION |
d207808916 | We show that a variety of modern deep learning tasks exhibit a "double-descent" phenomenon where, as we increase model size, performance first gets worse and then gets better. Moreover, we show that double descent occurs not just as a function of model size, but also as a function of the number of training epochs. We u... | DEEP DOUBLE DESCENT: WHERE BIGGER MODELS AND MORE DATA HURT |
d235377133 | In ordinary distillation, student networks are trained with soft labels (SLs) given by pretrained teacher networks, and students are expected to improve upon teachers since SLs are stronger supervision than the original hard labels. However, when considering adversarial robustness, teachers may become unreliable and ad... | RELIABLE ADVERSARIAL DISTILLATION WITH UNRELIABLE TEACHERS |
d202888772 | The performance of graph neural nets (GNNs) is known to gradually decrease with increasing number of layers. This decay is partly attributed to oversmoothing, where repeated graph convolutions eventually make node embeddings indistinguishable. We take a closer look at two different interpretations, aiming to quantify o... | PAIRNORM: TACKLING OVERSMOOTHING IN GNNS |
d258564452 | Hopfield networks are artificial neural networks which store memory patterns on the states of their neurons by choosing recurrent connection weights and update rules such that the energy landscape of the network forms attractors around the memories. How many stable, sufficiently-attracting memory patterns can we store ... | SIMPLICIAL HOPFIELD NETWORKS |
d53340521 | While many approaches to make neural networks more fathomable have been proposed, they are restricted to interrogating the network with input data. Measures for characterizing and monitoring structural properties, however, have not been developed. In this work, we propose neural persistence, a complexity measure for ne... | NEURAL PERSISTENCE: A COMPLEXITY MEASURE FOR DEEP NEURAL NETWORKS USING ALGEBRAIC TOPOLOGY |
d54482591 | We propose DeepV2D, an end-to-end differentiable deep learning architecture for predicting depth from a video sequence. We incorporate elements of classical Structure from Motion into an end-to-end trainable pipeline by designing a set of differentiable geometric modules. Our full system alternates between predicting d... | DeepV2D: Video to Depth with Differentiable Structure from Motion |
d247922520 | Large pretrained (e.g., "foundation") models exhibit distinct capabilities depending on the domain of data they are trained on. While these domains are generic, they may only barely overlap. For example, visual-language models (VLMs) are trained on Internet-scale image captions, but large language models (LMs) are furt... | Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language |
d209936257 | We describe a simple and general neural network weight compression approach, in which the network parameters (weights and biases) are represented in a "latent" space, amounting to a reparameterization. This space is equipped with a learned probability model, which is used to impose an entropy penalty on the parameter r... | SCALABLE MODEL COMPRESSION BY ENTROPY PENALIZED REPARAMETERIZATION |
d259203582 | In this paper, we propose a novel approach to conformal prediction for generative language models (LMs). Standard conformal prediction produces prediction sets-in place of single predictions-that have rigorous, statistical performance guarantees. LM responses are typically sampled from the model's predicted distributio... | Conformal Language Modeling |
d225068405 | Predicting the behaviors of Hamiltonian systems has been drawing increasing attention in scientific machine learning. However, the vast majority of the literature was focused on predicting separable Hamiltonian systems with their kinematic and potential energy terms being explicitly decoupled while building data-driven... | Nonseparable Symplectic Neural Networks |
d6790140 | We consider structure discovery of undirected graphical models from observational data. Inferring likely structures from few examples is a complex task often requiring the formulation of priors and sophisticated inference procedures. Popular methods rely on estimating a penalized maximum likelihood of the precision mat... | Learning to Discover Sparse Graphical Models |
d58028789 | Sequence-to-sequence models are commonly trained via maximum likelihood estimation (MLE). However, standard MLE training considers a word-level objective, predicting the next word given the previous ground-truth partial sentence. This procedure focuses on modeling local syntactic patterns, and may fail to capture long-... | IMPROVING SEQUENCE-TO-SEQUENCE LEARNING VIA OPTIMAL TRANSPORT |
d239009915 | Graph neural networks (GNNs) have become the standard learning architectures for graphs. GNNs have been applied to numerous domains ranging from quantum chemistry, recommender systems to knowledge graphs and natural language processing. A major issue with arbitrary graphs is the absence of canonical positional informat... | GRAPH NEURAL NETWORKS WITH LEARNABLE STRUCTURAL AND POSITIONAL REPRESENTATIONS |
d257102944 | We present an unsupervised deep learning model for 3D object classification. Conventional Hebbian learning, a well-known unsupervised model, suffers from loss of local features leading to reduced performance for tasks with complex geometric objects. We present a deep network with a novel Neuron Activity Aware (NeAW) He... | UNSUPERVISED 3D OBJECT LEARNING THROUGH NEURON ACTIVITY AWARE PLASTICITY |
d247158269 | We introduce SignNet and BasisNet-new neural architectures that are invariant to two key symmetries displayed by eigenvectors: (i) sign flips, since if v is an eigenvector then so is −v; and (ii) more general basis symmetries, which occur in higher dimensional eigenspaces with infinitely many choices of basis eigenvect... | SIGN AND BASIS INVARIANT NETWORKS FOR SPECTRAL GRAPH REPRESENTATION LEARNING |
d11591887 | Usually bilingual word vectors are trained "online".Mikolov et al. (2013a)showed they can also be found "offline"; whereby two pre-trained embeddings are aligned with a linear transformation, using dictionaries compiled from expert knowledge. In this work, we prove that the linear transformation between two spaces shou... | OFFLINE BILINGUAL WORD VECTORS, ORTHOGONAL TRANSFORMATIONS AND THE INVERTED SOFTMAX |
d238744187 | We aim at advancing open-vocabulary object detection, which detects objects described by arbitrary text inputs. The fundamental challenge is the availability of training data. It is costly to further scale up the number of classes contained in existing object detection datasets. To overcome this challenge, we propose V... | OPEN-VOCABULARY OBJECT DETECTION VIA VISION AND LANGUAGE KNOWLEDGE DISTILLATION |
d231951553 | Domain translation is the process of transforming data from one domain to another while preserving the common semantics. Some of the most popular domain translation systems are based on conditional generative adversarial networks, which use source domain data to drive the generator and as an input to the discriminator.... | DINO: A CONDITIONAL ENERGY-BASED GAN FOR DOMAIN TRANSLATION |
d67855277 | We present a method that learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of interacting agents. Our method is based on a graph-structured variational recurrent neural network (Graph-VRNN), which is trained end-to-end... | STOCHASTIC PREDICTION OF MULTI-AGENT INTERACTIONS FROM PARTIAL OBSERVATIONS |
d4560095 | Momentum is a simple and widely used trick which allows gradient-based optimizers to pick up speed along low curvature directions. Its performance depends crucially on a damping coefficient β. Large β values can potentially deliver much larger speedups, but are prone to oscillations and instability; hence one typically... | Aggregated Momentum: Stability Through Passive Damping |
d204512445 | We propose vq-wav2vec to learn discrete representations of audio segments through a wav2vec-style self-supervised context prediction task. The algorithm uses either a Gumbel-Softmax or online k-means clustering to quantize the dense representations. Discretization enables the direct application of algorithms from the N... | VQ-WAV2VEC: SELF-SUPERVISED LEARNING OF DISCRETE SPEECH REPRESENTATIONS |
d258960101 | Large language models (LLMs) have notably enhanced the fluency and diversity of machine-generated text.However, this progress also presents a significant challenge in detecting the origin of a given text, and current research on detection methods lags behind the rapid evolution of LLMs.Conventional training-based metho... | DNA-GPT: DIVERGENT N-GRAM ANALYSIS FOR TRAINING-FREE DETECTION OF GPT-GENERATED TEXT |
d247748837 | Diffusion probabilistic models (DPMs) and their extensions have emerged as competitive generative models yet confront challenges of efficient sampling. We propose a new bilateral denoising diffusion model (BDDM) that parameterizes both the forward and reverse processes with a schedule network and a score network, which... | BDDM: BILATERAL DENOISING DIFFUSION MODELS FOR FAST AND HIGH-QUALITY SPEECH SYNTHESIS |
d164019 | An important aspect of developing conversational agents is to give a bot the ability to improve through communicating with humans and to learn from the mistakes that it makes. Most research has focused on learning from fixed training sets of labeled data rather than interacting with a dialogue partner in an online fash... | DIALOGUE LEARNING WITH HUMAN-IN-THE-LOOP |
d52904249 | We propose to execute deep neural networks (DNNs) with dynamic and sparse graph (DSG) structure for compressive memory and accelerative execution during both training and inference. The great success of DNNs motivates the pursuing of lightweight models for the deployment onto embedded devices. However, most of the prev... | Dynamic Sparse Graph for Efficient Deep Learning |
d228372368 | Obtaining large annotated datasets is critical for training successful machine learning models and it is often a bottleneck in practice. Weak supervision offers a promising alternative for producing labeled datasets without ground truth annotations by generating probabilistic labels using multiple noisy heuristics. Thi... | INTERACTIVE WEAK SUPERVISION: LEARNING USEFUL HEURISTICS FOR DATA LABELING |
d256808676 | Real-world data usually couples the label ambiguity and heavy imbalance, challenging the algorithmic robustness of partial label learning (PLL) and long-tailed learning (LT). The straightforward combination of LT and PLL, i.e., LT-PLL, suffers from a fundamental dilemma: LT methods build upon a given class distribution... | LONG-TAILED PARTIAL LABEL LEARNING VIA DYNAMIC REBALANCING |
d252692952 | Unlike vision and language data which usually has a unique format, molecules can naturally be characterized using different chemical formulations. One can view a molecule as a 2D graph or define it as a collection of atoms located in a 3D space. For molecular representation learning, most previous works designed neural... | ONE TRANSFORMER CAN UNDERSTAND BOTH 2D & 3D MOLECULAR DATA |
d91184200 | As deep reinforcement learning driven by visual perception becomes more widely used there is a growing need to better understand and probe the learned agents. Understanding the decision making process and its relationship to visual inputs can be very valuable to identify problems in learned behavior. However, this topi... | Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents |
d3531730 | We present a generalization bound for feedforward neural networks in terms of the product of the spectral norms of the layers and the Frobenius norm of the weights. The generalization bound is derived using a PAC-Bayes analysis. | A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks |
d247627714 | The right to be forgotten has been legislated in many countries, but its enforcement in the AI industry would cause unbearable costs. When single data deletion requests come, companies may need to delete the whole models learned with massive resources. Existing works propose methods to remove knowledge learned from dat... | KNOWLEDGE REMOVAL IN SAMPLING-BASED BAYESIAN INFERENCE |
d262465392 | We present REMASKER, a new method of imputing missing values in tabular data by extending the masked autoencoding framework.Compared with prior work, REMASKER is both simple -besides the missing values (i.e., naturally masked), we randomly "re-mask" another set of values, optimize the autoencoder by reconstructing this... | ReMasker: Imputing Tabular Data with Masked Autoencoding |
d3455952 | For computer vision applications, prior works have shown the efficacy of reducing numeric precision of model parameters (network weights) in deep neural networks. Activation maps, however, occupy a large memory footprint during both the training and inference step when using mini-batches of inputs. One way to reduce th... | WRPN: Wide Reduced-Precision Networks |
d260501512 | We present a method for transferring neural representations from label-rich source domains to unlabeled target domains. Recent adversarial methods proposed for this task learn to align features across domains by fooling a special domain critic network. However, a drawback of this approach is that the critic simply labe... | ADVERSARIAL DROPOUT REGULARIZATION |
d263609239 | Solving a linear system Ax = b is a fundamental scientific computing primitive for which numerous solvers and preconditioners have been developed.These come with parameters whose optimal values depend on the system being solved and are often impossible or too expensive to identify; thus in practice sub-optimal heuristi... | LEARNING TO RELAX: SETTING SOLVER PARAMETERS ACROSS A SEQUENCE OF LINEAR SYSTEM INSTANCES |
d227151564 | Uncertainty quantification is crucial for building reliable and trustable machine learning systems. We propose to estimate uncertainty in recurrent neural networks (RNNs) via stochastic discrete state transitions over recurrent timesteps. The uncertainty of the model can be quantified by running a prediction several ti... | UNCERTAINTY ESTIMATION AND CALIBRATION WITH FINITE-STATE PROBABILISTIC RNNS |
d252917661 | Recently, diffusion models have emerged as a new paradigm for generative models. Despite the success in domains using continuous signals such as vision and audio, adapting diffusion models to natural language is under-explored due to the discrete nature of texts, especially for conditional generation. We tackle this ch... | DIFFUSEQ: SEQUENCE TO SEQUENCE TEXT GENERATION WITH DIFFUSION MODELS |
d263831633 | Supervised Fine-Tuning (SFT) on response demonstrations combined with Reinforcement Learning from Human Feedback (RLHF) constitutes a powerful paradigm for aligning LLM-based AI agents. However, a significant limitation of such an approach is its dependency on high-quality human annotations, making its application to i... | SALMON: SELF-ALIGNMENT WITH PRINCIPLE-FOLLOWING REWARD MODELS |
d258331830 | The process of revising (or constructing) a policy immediately prior to executionknown as decision-time planning-is key to achieving superhuman performance in perfect-information settings like chess and Go. A recent line of work has extended decision-time planning to more general imperfect-information settings, leading... | The Update Equivalence Framework for Decision-Time Planning |
d263620510 | Large Language Models (LLMs) have recently gained popularity due to their impressive few-shot performance across various downstream tasks.However, fine-tuning all parameters and storing a unique model for each downstream task or domain becomes impractical because of the massive size of checkpoints (e.g., 350GB in GPT-3... | NOLA: NETWORKS AS LINEAR COMBINATION OF LOW RANK RANDOM BASIS |
d260683028 | We present a novel unified bilevel optimization-based framework, PARL, formulated to address the recently highlighted critical issue of policy alignment in reinforcement learning using utility or preferencebased feedback.We identify a major gap within current algorithmic designs for solving policy alignment due to a la... | PARL: A Unified Framework for Policy Alignment in Reinforcement Learning |
d220525308 | A central challenge in developing versatile machine learning systems is catastrophic forgetting -a model trained on tasks in sequence will suffer significant performance drops on earlier tasks. Despite the ubiquity of catastrophic forgetting, there is limited understanding of the underlying process and its causes. In t... | Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics Google Mountain View, CA Google Mountain View, CA |
d3643430 | Deep learning networks have achieved state-of-the-art accuracies on computer vision workloads like image classification and object detection. The performant systems, however, typically involve big models with numerous parameters. Once trained, a challenging aspect for such top performing models is deployment on resourc... | Apprentice: Using KD Techniques to Improve Low-Precision Network Accuracy APPRENTICE: USING KNOWLEDGE DISTILLATION TECHNIQUES TO IMPROVE LOW-PRECISION NET- WORK ACCURACY |
d263874273 | Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively.Recently, Deep Clustering gained popularity due to the non-linearity of neural networks, which allows for flexibility in fitting the specific peculiarities of complex data.Here we int... | MIXTURE-OF-EXPERTS VARIATIONAL AUTOENCODER FOR CLUSTERING AND GENERATING FROM SIMILARITY-BASED REPRESENTATIONS A PREPRINT |
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