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d3300937 | Supervised learning depends on annotated examples, which are taken to be the ground truth. But these labels often come from noisy crowdsourcing platforms, like Amazon Mechanical Turk. Practitioners typically collect multiple labels per example and aggregate the results to mitigate noise (the classic crowdsourcing probl... | Learning From Noisy Singly-labeled Data |
d53216170 | Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input. Based on the promising results of graph neural networks on highly structured data, we develop a framework to extend existing sequence encoders with a graph compone... | STRUCTURED NEURAL SUMMARIZATION |
d220424510 | Despite significant advances, continual learning models still suffer from catastrophic forgetting when exposed to incrementally available data from non-stationary distributions. Rehearsal approaches alleviate the problem by maintaining and replaying a small episodic memory of previous samples, often implemented as an a... | Graph-Based Continual Learning |
d174802369 | Continual learning aims to learn new tasks without forgetting previously learned ones. This is especially challenging when one cannot access data from previous tasks and when the model has a fixed capacity. Current regularization-based continual learning algorithms need an external representation and extra computation ... | UNCERTAINTY-GUIDED CONTINUAL LEARNING WITH BAYESIAN NEURAL NETWORKS |
d260704206 | The legality of training language models (LMs) on copyrighted or otherwise restricted data is under intense debate. However, as we show, model performance significantly degrades if trained only on low-risk text (e.g., out-of-copyright books or government documents), due to its limited size and domain coverage. We prese... | SILO LANGUAGE MODELS: ISOLATING LEGAL RISK IN A NONPARAMETRIC DATASTORE |
d252715596 | We address the problem of safe reinforcement learning from pixel observations. Inherent challenges in such settings are (1) a trade-off between reward optimization and adhering to safety constraints, (2) partial observability, and (3) high-dimensional observations. We formalize the problem in a constrained, partially o... | SAFE REINFORCEMENT LEARNING FROM PIXELS USING A STOCHASTIC LATENT REPRESENTATION |
d221112239 | Quantum computing-based machine learning mainly focuses on quantum computing hardware that is experimentally challenging to realize due to requiring quantum gates that operate at very low temperature. Instead, we demonstrate the existence of a lower performance and much lower effort island on the accuracy-vs-qubits gra... | Single-Photon Image Classification |
d4737664 | The ability of algorithms to evolve or learn (compositional) communication protocols has traditionally been studied in the language evolution literature through the use of emergent communication tasks. Here we scale up this research by using contemporary deep learning methods and by training reinforcement-learning neur... | EMERGENCE OF LINGUISTIC COMMUNICATION FROM REFERENTIAL GAMES WITH SYMBOLIC AND PIXEL INPUT |
d247084493 | Image denoising and artefact removal are complex inverse problems admitting multiple valid solutions. Unsupervised diversity restoration, that is, obtaining a diverse set of possible restorations given a corrupted image, is important for ambiguity removal in many applications such as microscopy where paired data for su... | INTERPRETABLE UNSUPERVISED DIVERSITY DENOISING AND ARTEFACT REMOVAL |
d247594371 | We propose a family of adaptive integer compression operators for distributed Stochastic Gradient Descent (SGD) that do not communicate a single float. This is achieved by multiplying floating-point vectors with a number known to every device and then rounding to integers. In contrast to the prior work on integer compr... | INTSGD: ADAPTIVE FLOATLESS COMPRESSION OF STOCHASTIC GRADIENTS |
d209475822 | Most research on lifelong learning applies to images or games, but not language. We present LAMOL, a simple yet effective method for lifelong language learning (LLL) based on language modeling. LAMOL replays pseudo-samples of previous tasks while requiring no extra memory or model capacity. Specifically, LAMOL is a lan... | LAMOL: LANGUAGE MODELING FOR LIFELONG LANGUAGE LEARNING |
d88522730 | In this paper we approach two relevant deep learning topics: i) tackling of graph structured input data and ii) a better understanding and analysis of deep networks and related learning algorithms. With this in mind we focus on the topological classification of reachability in a particular subset of planar graphs (Maze... | CRITICAL PERCOLATION AS A FRAMEWORK TO ANALYZE THE TRAINING OF DEEP NETWORKS |
d238419044 | This paper follows up on a recent work of Neu et al. (2021) and presents some new information-theoretic upper bounds for the generalization error of machine learning models, such as neural networks, trained with SGD. We apply these bounds to analyzing the generalization behaviour of linear and two-layer ReLU networks.... | ON THE GENERALIZATION OF MODELS TRAINED WITH SGD: INFORMATION-THEORETIC BOUNDS AND IMPLICATIONS |
d52922363 | We analyze speed of convergence to global optimum for gradient descent training a deep linear neural network (parameterized as x → W N W N −1 · · · W 1 x) by minimizing the 2 loss over whitened data. Convergence at a linear rate is guaranteed when the following hold: (i) dimensions of hidden layers are at least the min... | A CONVERGENCE ANALYSIS OF GRADIENT DESCENT FOR DEEP LINEAR NEURAL NETWORKS |
d3323727 | Deep neural networks (DNNs) continue to make significant advances, solving tasks from image classification to translation or reinforcement learning. One aspect of the field receiving considerable attention is efficiently executing deep models in resource-constrained environments, such as mobile or embedded devices. Thi... | MODEL COMPRESSION VIA DISTILLATION AND QUANTIZATION |
d257280442 | What is an image and how to extract latent features? Convolutional Networks (ConvNets) consider an image as organized pixels in a rectangular shape and extract features via convolutional operation in local region; Vision Transformers (ViTs) treat an image as a sequence of patches and extract features via attention mech... | Image as Set of Points |
d219721263 | We present Wasserstein Embedding for Graph Learning (WEGL), a novel and fast framework for embedding entire graphs in a vector space, in which various machine learning models are applicable for graph-level prediction tasks. We leverage new insights on defining similarity between graphs as a function of the similarity b... | Wasserstein Embedding for Graph Learning |
d256900870 | Data augmentation is one of the most prevalent tools in deep learning, underpinning many recent advances, including those from classification, generative models, and representation learning. The standard approach to data augmentation combines simple transformations like rotations and flips to generate new images from e... | Effective Data Augmentation With Diffusion Models |
d239009938 | In the Mixup training paradigm, a model is trained using convex combinations of data points and their associated labels. Despite seeing very few true data points during training, models trained using Mixup seem to still minimize the original empirical risk and exhibit better generalization and robustness on various tas... | TOWARDS UNDERSTANDING THE DATA DEPENDENCY OF MIXUP-STYLE TRAINING |
d238354201 | Light-weight convolutional neural networks (CNNs) are the de-facto for mobile vision tasks. Their spatial inductive biases allow them to learn representations with fewer parameters across different vision tasks. However, these networks are spatially local. To learn global representations, self-attention-based vision tr... | MOBILEVIT: LIGHT-WEIGHT, GENERAL-PURPOSE, AND MOBILE-FRIENDLY VISION TRANSFORMER |
d263608332 | Training AI models that generalize across tasks and domains has long been among the open problems driving AI research.The emergence of Foundation Models made it easier to obtain expert models for a given task, but the heterogeneity of data that may be encountered at test time often means that any single expert is insuf... | FUSING MODELS WITH COMPLEMENTARY EXPERTISE |
d49868626 | Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems. However, they have practical difficulties when operating on high-dimensional parameter spaces in extreme low-data regimes. We show that it is possible to bypass these ... | META-LEARNING WITH LATENT EMBEDDING OPTIMIZATION |
d44095973 | Solving tasks in Reinforcement Learning is no easy feat. As the goal of the agent is to maximize the accumulated reward, it often learns to exploit loopholes and misspecifications in the reward signal resulting in unwanted behavior. While constraints may solve this issue, there is no closed form solution for general co... | Reward Constrained Policy Optimization |
d53802740 | This paper introduces a new framework for data efficient and versatile learning. Specifically: 1) We develop ML-PIP, a general framework for Meta-Learning approximate Probabilistic Inference for Prediction. ML-PIP extends existing probabilistic interpretations of meta-learning to cover a broad class of methods. 2) We i... | META-LEARNING PROBABILISTIC INFERENCE FOR PREDICTION |
d246431014 | Methods that combine local and global features have recently shown excellent performance on multiple challenging deep image retrieval benchmarks, but their use of local features raises at least two issues. First, these local features simply boil down to the localized map activations of a neural network, and hence can b... | LEARNING SUPER-FEATURES FOR IMAGE RETRIEVAL |
d236635379 | A central goal of machine learning is the development of systems that can solve many problems in as many data domains as possible. Current architectures, however, cannot be applied beyond a small set of stereotyped settings, as they bake in domain & task assumptions or scale poorly to large inputs or outputs. In this w... | PERCEIVER IO: A GENERAL ARCHITECTURE FOR STRUCTURED INPUTS & OUTPUTS |
d238215172 | It is widely believed that the implicit regularization of SGD is fundamental to the impressive generalization behavior we observe in neural networks. In this work, we demonstrate that non-stochastic full-batch training can achieve comparably strong performance to SGD on CIFAR-10 using modern architectures. To this end,... | STOCHASTIC TRAINING IS NOT NECESSARY FOR GENERALIZATION |
d3526391 | We consider the problem of detecting out-of-distribution images in neural networks. We propose ODIN, a simple and effective method that does not require any change to a pre-trained neural network. Our method is based on the observation that using temperature scaling and adding small perturbations to the input can separ... | ENHANCING THE RELIABILITY OF OUT-OF-DISTRIBUTION IMAGE DETECTION IN NEURAL NETWORKS |
d52935027 | Simulation is a useful tool in situations where training data for machine learning models is costly to annotate or even hard to acquire. In this work, we propose a reinforcement learning-based method for automatically adjusting the parameters of any (non-differentiable) simulator, thereby controlling the distribution o... | LEARNING TO SIMULATE |
d264172668 | Large Language Models (LLMs) inherently encode a wealth of knowledge within their parameters through pre-training on extensive corpora. While prior research has delved into operations on these parameters to manipulate the underlying implicit knowledge-encompassing detection, editing, and merging-there remains an ambigu... | SEEKING NEURAL NUGGETS: KNOWLEDGE TRANSFER IN LARGE LANGUAGE MODELS FROM A PARAMETRIC PERSPECTIVE |
d258418258 | Sharpness-aware minimization (SAM), which searches for flat minima by min-max optimization, has been shown to be useful in improving model generalization. However, since each SAM update requires computing two gradients, its computational cost and training time are both doubled compared to standard empirical risk minimi... | AN ADAPTIVE POLICY TO EMPLOY SHARPNESS-AWARE MINIMIZATION |
d86840468 | We study the problem of learning to map, in an unsupervised way, between domains A and B, such that the samples b ∈ B contain all the information that exists in samples a ∈ A and some additional information. For example, ignoring occlusions, B can be people with glasses, A people without, and the glasses, would be the ... | EMERGING DISENTANGLEMENT IN AUTO-ENCODER BASED UNSUPERVISED IMAGE CONTENT TRANSFER |
d247476014 | Monocular 3D object detection is one of the most challenging tasks in 3D scene understanding. Due to the ill-posed nature of monocular imagery, existing monocular 3D detection methods highly rely on training with the manually annotated 3D box labels on the LiDAR point clouds. This annotation process is very laborious a... | WEAKM3D: TOWARDS WEAKLY SUPERVISED MONOCULAR 3D OBJECT DETECTION |
d261076339 | It is now possible to reconstruct dynamic human motion and shape from a sparse set of cameras using Neural Radiance Fields (NeRF) driven by an underlying skeleton. However, a challenge remains to model the deformation of cloth and skin in relation to skeleton pose. Unlike existing avatar models that are learned implici... | POSE MODULATED AVATARS FROM VIDEO |
d249954052 | One of the main challenges for feature representation in deep learning-based classification is the design of appropriate loss functions that exhibit strong discriminative power. The classical softmax loss does not explicitly encourage discriminative learning of features. A popular direction of research is to incorporat... | LEARNING TOWARDS THE LARGEST MARGINS |
d250089240 | Molecular representation pretraining is critical in various applications for drug and material discovery due to the limited number of labeled molecules, and most existing work focuses on pretraining on 2D molecular graphs. However, the power of pretraining on 3D geometric structures has been less explored. This is owin... | MOLECULAR GEOMETRY PRETRAINING WITH SE(3)-INVARIANT DENOISING DISTANCE MATCHING |
d263620365 | Semi-supervised learning (SSL) has witnessed great progress with various improvements in the self-training framework with pseudo labeling.The main challenge is how to distinguish high-quality pseudo labels against the confirmation bias.However, existing pseudo-label selection strategies are limited to pre-defined schem... | SEMIREWARD: A GENERAL REWARD MODEL FOR SEMI-SUPERVISED LEARNING |
d221139573 | Generative modeling has recently shown great promise in computer vision, but its success is often limited to separate tasks. In this paper, motivated by multi-task learning of shareable feature representations, we consider a novel problem of learning a shared generative model across various tasks. We instantiate it on ... | Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis |
d238531318 | Transformers are transforming the landscape of computer vision, especially for recognition tasks. Detection transformers are the first fully end-to-end learning systems for object detection, while vision transformers are the first fully transformer-based architecture for image classification. In this paper, we integrat... | VIDT: AN EFFICIENT AND EFFECTIVE FULLY TRANSFORMER-BASED OBJECT DETECTOR |
d238857129 | Graph neural networks (GNNs) and label propagation represent two interrelated modeling strategies designed to exploit graph structure in tasks such as node property prediction. The former is typically based on stacked message-passing layers that share neighborhood information to transform node features into predictive ... | WHY PROPAGATE ALONE? PARALLEL USE OF LABELS AND FEATURES ON GRAPHS |
d239050028 | Noise-contrastive estimation (NCE) is a statistically consistent method for learning unnormalized probabilistic models. It has been empirically observed that the choice of the noise distribution is crucial for NCE's performance. However, such observations have never been made formal or quantitative. In fact, it is not ... | Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation |
d222291282 | Gradient estimation in models with discrete latent variables is a challenging problem, because the simplest unbiased estimators tend to have high variance. To counteract this, modern estimators either introduce bias, rely on multiple function evaluations, or use learned, input-dependent baselines. Thus, there is a need... | RAO-BLACKWELLIZING THE STRAIGHT-THROUGH GUMBEL-SOFTMAX GRADIENT ESTIMATOR |
d251320513 | We extend conformal prediction to control the expected value of any monotone loss function. The algorithm generalizes split conformal prediction together with its coverage guarantee. Like conformal prediction, the conformal risk control procedure is tight up to an O(1/n) factor. We also introduce extensions of the idea... | Conformal Risk Control |
d257280401 | We present a general framework for evaluating image counterfactuals. The power and flexibility of deep generative models make them valuable tools for learning mechanisms in structural causal models. However, their flexibility makes counterfactual identifiability impossible in the general case. Motivated by these issues... | MEASURING AXIOMATIC SOUNDNESS OF COUNTERFACTUAL IMAGE MODELS |
d2263947 | We propose a method to optimize the representation and distinguishability of samples from two probability distributions, by maximizing the estimated power of a statistical test based on the maximum mean discrepancy (MMD). This optimized MMD is applied to the setting of unsupervised learning by generative adversarial ne... | GENERATIVE MODELS AND MODEL CRITICISM VIA OPTIMIZED MAXIMUM MEAN DISCREPANCY |
d235606384 | In the paper, we design a novel Bregman gradient policy optimization framework for reinforcement learning based on Bregman divergences and momentum techniques. Specifically, we propose a Bregman gradient policy optimization (BGPO) algorithm based on the basic momentum technique and mirror descent iteration. Meanwhile, ... | BREGMAN GRADIENT POLICY OPTIMIZATION |
d52077536 | Prediction is arguably one of the most basic functions of an intelligent system. In general, the problem of predicting events in the future or between two waypoints is exceedingly difficult. However, most phenomena naturally pass through relatively predictable bottlenecks-while we cannot predict the precise trajectory ... | TIME-AGNOSTIC PREDICTION: PREDICTING PREDICTABLE VIDEO FRAMES |
d252780718 | Tabular data synthesis is a long-standing research topic in machine learning. Many different methods have been proposed over the past decades, ranging from statistical methods to deep generative methods. However, it has not always been successful due to the complicated nature of real-world tabular data. In this paper, ... | STASY: SCORE-BASED TABULAR DATA SYNTHESIS |
d252693505 | Output reachability and adversarial robustness are among the most relevant safety properties of neural networks. We show that in the context of Message Passing Neural Networks (MPNN), a common Graph Neural Network (GNN) model, formal verification is impossible. In particular, we show that output reachability of graph-c... | FUNDAMENTAL LIMITS IN FORMAL VERIFICATION OF MESSAGE-PASSING NEURAL NETWORKS |
d250144478 | Semi-Supervised Learning (SSL) is fundamentally a missing label problem, in which the label Missing Not At Random (MNAR) problem is more realistic and challenging, compared to the widely-adopted yet naïve Missing Completely At Random assumption where both labeled and unlabeled data share the same class distribution. Di... | ON NON-RANDOM MISSING LABELS IN SEMI-SUPERVISED LEARNING |
d231918454 | Isotropic Gaussian priors are the de facto standard for modern Bayesian neural network inference. However, it is unclear whether these priors accurately reflect our true beliefs about the weight distributions or give optimal performance. To find better priors, we study summary statistics of neural network weights in ne... | BAYESIAN NEURAL NETWORK PRIORS REVISITED |
d232307359 | Source code (Context) and its parsed abstract syntax tree (AST; Structure) are two complementary representations of the same computer program. Traditionally, designers of machine learning models have relied predominantly either on Structure or Context. We propose a new model, which jointly learns on Context and Structu... | LANGUAGE-AGNOSTIC REPRESENTATION LEARNING OF SOURCE CODE FROM STRUCTURE AND CONTEXT |
d256274566 | This paper proposes a simple method to distill and detect backdoor patterns within an image: Cognitive Distillation (CD). The idea is to extract the "minimal essence" from an input image responsible for the model's prediction. CD optimizes an input mask to extract a small pattern from the input image that can lead to t... | DISTILLING COGNITIVE BACKDOOR PATTERNS WITHIN AN IMAGE |
d251104701 | We present Generalizable NeRF Transformer (GNT), a transformer-based architecture that reconstructs Neural Radiance Fields (NeRFs) and learns to render novel views on the fly from source views. While prior works on NeRFs optimize a scene representation by inverting a handcrafted rendering equation, GNT achieves neural ... | IS ATTENTION ALL THAT NERF NEEDS? |
d231802365 | Conventional neural architectures for sequential data present important limitations. Recurrent neural networks suffer from exploding and vanishing gradients, small effective memory horizons, and must be trained sequentially. Convolutional neural networks cannot handle sequences of unknown size and their memory horizon ... | CKCONV: CONTINUOUS KERNEL CONVOLUTION FOR SEQUENTIAL DATA |
d260611249 | Recent months have seen the emergence of a powerful new trend in which large language models (LLMs) are augmented to become autonomous language agents capable of performing objective oriented multi-step tasks on their own, rather than merely responding to queries from human users. Most existing language agents, however... | RETROFORMER: RETROSPECTIVE LARGE LANGUAGE AGENTS WITH POLICY GRADIENT OPTIMIZATION |
d54462139 | Representations of sets are challenging to learn because operations on sets should be permutation-invariant. To this end, we propose a Permutation-Optimisation module that learns how to permute a set end-to-end. The permuted set can be further processed to learn a permutation-invariant representation of that set, avoid... | LEARNING REPRESENTATIONS OF SETS THROUGH OPTIMIZED PERMUTATIONS |
d238418995 | Recent empirical advances show that training deep models with large learning rate often improves generalization performance. However, theoretical justifications on the benefits of large learning rate are highly limited, due to challenges in analysis. In this paper, we consider using Gradient Descent (GD) with a large l... | LARGE LEARNING RATE TAMES HOMOGENEITY: CONVERGENCE AND BALANCING EFFECT |
d249062882 | In the mode connectivity literature, it is widely accepted that there are common circumstances in which two neural networks, trained similarly on the same data, will maintain loss when interpolated in the weight space. In particular, transfer learning is presumed to ensure the necessary conditions for linear mode conne... | LINEAR CONNECTIVITY REVEALS GENERALIZATION STRATEGIES |
d257834100 | Few datasets contain self-identified sensitive attributes, inferring attributes risks introducing additional biases, and collecting attributes can carry legal risks. Besides, categorical labels can fail to reflect the continuous nature of human phenotypic diversity, making it difficult to compare the similarity between... | A VIEW FROM SOMEWHERE: HUMAN-CENTRIC FACE REPRESENTATIONS |
d247594823 | Reward-free, unsupervised discovery of skills is an attractive alternative to the bottleneck of hand-designing rewards in environments where task supervision is scarce or expensive. However, current skill pre-training methods, like many RL techniques, make a fundamental assumption -stationary environments during traini... | ONE AFTER ANOTHER: LEARNING INCREMENTAL SKILLS FOR A CHANGING WORLD |
d229348988 | We propose a Distributional Approach for addressing Controlled Text Generation from pre-trained Language Models (LMs). This approach permits to specify, in a single formal framework, both "pointwise" and "distributional" constraints over the target LM -to our knowledge, the first model with such generalitywhile minimiz... | A DISTRIBUTIONAL APPROACH TO CONTROLLED TEXT GENERATION |
d238582721 | Embedding learning has found widespread applications in recommendation systems and natural language modeling, among other domains. To learn quality embeddings efficiently, adaptive learning rate algorithms have demonstrated superior empirical performance over SGD, largely accredited to their token-dependent learning ra... | Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits |
d211010532 | With the recent success and popularity of pre-trained language models (LMs) in natural language processing, there has been a rise in efforts to understand their inner workings. In line with such interest, we propose a novel method that assists us in investigating the extent to which pre-trained LMs capture the syntacti... | ARE PRE-TRAINED LANGUAGE MODELS AWARE OF PHRASES? SIMPLE BUT STRONG BASELINES FOR GRAMMAR INDUCTION |
d232105154 | Adversarial attacks expose important vulnerabilities of deep learning models, yet little attention has been paid to settings where data arrives as a stream. In this paper, we formalize the online adversarial attack problem, emphasizing two key elements found in real-world use-cases: attackers must operate under partial... | ONLINE ADVERSARIAL ATTACKS |
d3461974 | Consider how easy it is for people to imagine what a "purple hippo" would look like, even though they do not exist. If we instead said "purple hippo with wings", they could just as easily create a different internal mental representation, to represent this more specific concept. To assess whether the person has correct... | Generative Models of Visually Grounded Imagination |
d17611960 | Modern convolutional networks, incorporating rectifiers and max-pooling, are neither smooth nor convex. Standard guarantees therefore do not apply. Nevertheless, methods from convex optimization such as gradient descent and Adam are widely used as building blocks for deep learning algorithms.This paper provides the fir... | Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks |
d264128411 | A fundamental characteristic of audio is its compositional nature.Audio-language models (ALMs) trained using a contrastive approach (e.g., CLAP) that learns a shared representation between audio and language modalities have improved performance in many downstream applications, including zero-shot audio classification, ... | COMPA: ADDRESSING THE GAP IN COMPOSITIONAL REASONING IN AUDIO-LANGUAGE MODELS |
d244527239 | Rate-distortion (R-D) function, a key quantity in information theory, characterizes the fundamental limit of how much a data source can be compressed subject to a fidelity criterion, by any compression algorithm. As researchers push for everimproving compression performance, establishing the R-D function of a given dat... | TOWARDS EMPIRICAL SANDWICH BOUNDS ON THE RATE-DISTORTION FUNCTION |
d67855286 | Recurrent neural networks have gained widespread use in modeling sequential data. Learning long-term dependencies using these models remains difficult though, due to exploding or vanishing gradients. In this paper, we draw connections between recurrent networks and ordinary differential equations. A special form of rec... | ANTISYMMETRICRNN: A DYNAMICAL SYSTEM VIEW ON RECURRENT NEURAL NETWORKS |
d257365137 | Generative flow networks (GFlowNets), as an emerging technique, can be used as an alternative to reinforcement learning for exploratory control tasks. GFlowNet aims to generate distribution proportional to the rewards over terminating states, and to sample different candidates in an active learning fashion. GFlowNets n... | CFLOWNETS: CONTINUOUS CONTROL WITH GENERATIVE FLOW NETWORKS |
d261582259 | Diffusion models achieved great success in image synthesis, but still face challenges in high-resolution generation. Through the lens of discrete cosine transformation, we find the main reason is that the same noise level on a higher resolution results in a higher Signal-to-Noise Ratio in the frequency domain. In this ... | RELAY DIFFUSION: UNIFYING DIFFUSION PROCESS ACROSS RESOLUTIONS FOR IMAGE SYNTHESIS |
d201657791 | Stochastic AUC maximization has garnered an increasing interest due to better fit to imbalanced data classification. However, existing works are limited to stochastic AUC maximization with a linear predictive model, which restricts its predictive power when dealing with extremely complex data. In this paper, we conside... | Stochastic AUC Maximization with Deep Neural Networks |
d53717167 | Deep neural networks (DNNs) have set benchmarks on a wide array of supervised learning tasks. Trained DNNs, however, often lack robustness to minor adversarial perturbations to the input, which undermines their true practicality. Recent works have increased the robustness of DNNs by fitting networks using adversarially... | Generalizable Adversarial Training via Spectral Normalization |
d220055784 | In this paper, we cast fair machine learning as invariant machine learning. We first formulate a version of individual fairness that enforces invariance on certain sensitive sets. We then design a transport-based regularizer that enforces this version of individual fairness and develop an algorithm to minimize the regu... | SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness |
d260519400 | Hyperbolic spaces, which have the capacity to embed tree structures without distortion owing to their exponential volume growth, have recently been applied to machine learning to better capture the hierarchical nature of data. In this study, we reconsider a way to generalize the fundamental components of neural network... | Hyperbolic Neural Networks++ |
d264590778 | Social alignment in AI systems aims to ensure that these models behave according to established societal values.However, unlike humans, who derive consensus on value judgments through social interaction, current language models (LMs) are trained to rigidly replicate their training corpus in isolation, leading to subpar... | TRAINING SOCIALLY ALIGNED LANGUAGE MODELS ON SIMULATED SOCIAL INTERACTIONS |
d240288910 | A key goal of unsupervised representation learning is "inverting" a data generating process to recover its latent properties. Existing work that provably achieves this goal relies on strong assumptions on relationships between the latent variables (e.g., independence conditional on auxiliary information). In this paper... | Properties from Mechanisms: An Equivariance Perspective on Identifiable Representation Learning |
d253080580 | Bridging geometry and topology, curvature is a powerful and expressive invariant. While the utility of curvature has been theoretically and empirically confirmed in the context of manifolds and graphs, its generalization to the emerging domain of hypergraphs has remained largely unexplored. On graphs, the Ollivier-Ricc... | OLLIVIER-RICCI CURVATURE FOR HYPERGRAPHS: A UNIFIED FRAMEWORK |
d244478155 | Vision Transformer (ViT) is emerging as the state-of-the-art architecture for image recognition. While recent studies suggest that ViTs are more robust than their convolutional counterparts, our experiments find that ViTs trained on ImageNet are overly reliant on local textures and fail to make adequate use of shape in... | DISCRETE REPRESENTATIONS STRENGTHEN VISION TRANSFORMER ROBUSTNESS |
d252682995 | Temporal networks model a variety of important phenomena involving timed interactions between entities. Existing methods for machine learning on temporal networks generally exhibit at least one of two limitations. First, time is assumed to be discretized, so if the time data is continuous, the user must determine the d... | Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs |
d249712405 | As machine learning becomes more widespread throughout society, aspects including data privacy and fairness must be carefully considered, and are crucial for deployment in highly regulated industries. Unfortunately, the application of privacy enhancing technologies can worsen unfair tendencies in models. In particular,... | DISPARATE IMPACT IN DIFFERENTIAL PRIVACY FROM GRADIENT MISALIGNMENT |
d8257350 | Recurrent neural networks have achieved excellent performance in many applications. However, on portable devices with limited resources, the models are often too large to deploy. For applications on the server with large scale concurrent requests, the latency during inference can also be very critical for costly comput... | ALTERNATING MULTI-BIT QUANTIZATION FOR RECURRENT NEURAL NETWORKS |
d3708505 | A deep fully-connected neural network with an i.i.d. prior over its parameters is equivalent to a Gaussian process (GP) in the limit of infinite network width. This correspondence enables exact Bayesian inference for neural networks on regression tasks by means of straightforward matrix computations. For single hiddenl... | DEEP NEURAL NETWORKS AS GAUSSIAN PROCESSES |
d263830446 | The optimal transport problem for measures supported on non-Euclidean spaces has recently gained ample interest in diverse applications involving representation learning.In this paper, we focus on circular probability measures, i.e., probability measures supported on the unit circle, and introduce a new computationally... | LCOT: Linear circular optimal transport |
d53477919 | Control of complex systems involves both system identification and controller design.Deep neural networks have proven to be successful in many identification tasks, such as classification, prediction, and end-to-end system modeling.However, from the controller design perspective, these networks are difficult to work wi... | Optimal Control Via Neural Networks: A Convex Approach |
d211132391 | Class-conditional generative models hold promise to overcome the shortcomings of their discriminative counterparts. They are a natural choice to solve discriminative tasks in a robust manner as they jointly optimize for predictive performance and accurate modeling of the input distribution. In this work, we investigate... | UNDERSTANDING THE LIMITATIONS OF CONDITIONAL GENERATIVE MODELS |
d231719892 | Deep generative modeling has seen impressive advances in recent years, to the point where it is now commonplace to see simulated samples (e.g., images) that closely resemble real-world data.However, generation quality is generally inconsistent for any given model and can vary dramatically between samples.We introduce D... | REFINING DEEP GENERATIVE MODELS VIA DISCRIMINATOR GRADIENT FLOW |
d53781800 | In this work, we address the problem of musical timbre transfer, where the goal is to manipulate the timbre of a sound sample from one instrument to match another instrument while preserving other musical content, such as pitch, rhythm, and loudness.In principle, one could apply image-based style transfer techniques to... | TIMBRETRON: A WAVENET(CYCLEGAN(CQT(AUDIO))) PIPELINE FOR MUSICAL TIMBRE TRANSFER |
d174797767 | Forming perceptual groups and individuating objects in visual scenes is an essential step towards visual intelligence. This ability is thought to arise in the brain from computations implemented by bottom-up, horizontal, and top-down connections between neurons. However, the relative contributions of these connections ... | Disentangling neural mechanisms for perceptual grouping |
d257050884 | Active learning has demonstrated data efficiency in many fields. Existing active learning algorithms, especially in the context of batch-mode deep Bayesian active models, rely heavily on the quality of uncertainty estimations of the model, and are often challenging to scale to large batches. In this paper, we propose B... | SCALABLE BATCH-MODE DEEP BAYESIAN ACTIVE LEARNING VIA EQUIVALENCE CLASS ANNEALING |
d17306137 | Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and model global structure well but have difficulty capturing small details. PixelCNN models details very well, but lacks a latent code and is difficult to scale for capturing la... | PIXELVAE: A LATENT VARIABLE MODEL FOR NATURAL IMAGES |
d53018855 | We propose a rejection sampling scheme using the discriminator of a GAN to approximately correct errors in the GAN generator distribution. We show that under quite strict assumptions, this will allow us to recover the data distribution exactly. We then examine where those strict assumptions break down and design a prac... | DISCRIMINATOR REJECTION SAMPLING |
d252992725 | Algorithmic reasoning requires capabilities which are most naturally understood through recurrent models of computation, like the Turing machine. However, Transformer models, while lacking recurrence, are able to perform such reasoning using far fewer layers than the number of reasoning steps. This raises the question:... | Transformers Learn Shortcuts to Automata |
d257232817 | We give the first efficient algorithm for learning halfspaces in the testable learning model recently defined by Rubinfeld and Vasilyan[RV23]. In this model, a learner certifies that the accuracy of its output hypothesis is near optimal whenever the training set passes an associated test, and training sets drawn from s... | An Efficient Tester-Learner for Halfspaces |
d225062170 | The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdowns for emergency containment. Large-scale digital contact tracing (DCT) 1 has emerged as a potential solution to resume economic and social activity while minimizing spread of the... | PREDICTING INFECTIOUSNESS FOR PROACTIVE CONTACT TRACING |
d52895832 | Adam is shown not being able to converge to the optimal solution in certain cases. Researchers recently propose several algorithms to avoid the issue of nonconvergence of Adam, but their efficiency turns out to be unsatisfactory in practice. In this paper, we provide a new insight into the non-convergence issue of Adam... | ADASHIFT: DECORRELATION AND CONVERGENCE OF ADAPTIVE LEARNING RATE METHODS |
d251564473 | The remarkable performance gains realized by large pretrained models, e.g., GPT-3, hinge on the massive amounts of data they are exposed to during training. Analogously, distilling such large models to compact models for efficient deployment also necessitates a large amount of (labeled or unlabeled) training data. In t... | Teacher Guided Training: An Efficient Framework for Knowledge Transfer |
d259313791 | This paper proposes a simple method to distill and detect backdoor patterns within an image: Cognitive Distillation (CD). The idea is to extract the "minimal essence" from an input image responsible for the model's prediction. CD optimizes an input mask to extract a small pattern from the input image that can lead to t... | DISTILLING COGNITIVE BACKDOOR PATTERNS WITHIN AN IMAGE |
d220127956 | Influence functions approximate the effect of training samples in test-time predictions and have a wide variety of applications in machine learning interpretability and uncertainty estimation. A commonly-used (first-order) influence function can be implemented efficiently as a post-hoc method requiring access only to t... | Influence Functions in Deep Learning Are Fragile |
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