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d235731899 | Recent works have revealed that infinitely-wide feed-forward or recurrent neural networks of any architecture correspond to Gaussian processes referred to as Neural Network Gaussian Processes (NNGPs). While these works have extended the class of neural networks converging to Gaussian processes significantly, however, t... | SCALE MIXTURES OF NEURAL NETWORK GAUSSIAN PROCESSES |
d54203451 | Numerous models for grounded language understanding have been recently proposed, including (i) generic models that can be easily adapted to any given task with little adaptation and (ii) intuitively appealing modular models that require background knowledge to be instantiated. We compare both types of models in how muc... | SYSTEMATIC GENERALIZATION: WHAT IS REQUIRED AND CAN IT BE LEARNED? |
d221761540 | We propose a webly-supervised representation learning method that does not suffer from the annotation unscalability of supervised learning, nor the computation unscalability of self-supervised learning. Most existing works on weblysupervised representation learning adopt a vanilla supervised learning method without acc... | MOPRO: WEBLY SUPERVISED LEARNING WITH MOMENTUM PROTOTYPES |
d238408412 | Multilingual models jointly pretrained on multiple languages have achieved remarkable performance on various multilingual downstream tasks. Moreover, models finetuned on a single monolingual downstream task have shown to generalize to unseen languages. In this paper, we first show that it is crucial for those tasks to ... | SEQUENTIAL REPTILE: INTER-TASK GRADIENT ALIGNMENT FOR MULTILINGUAL LEARNING |
d263909090 | This paper studies close-loop task planning, which refers to the process of generating a sequence of skills (a plan) to accomplish a specific goal while adapting the plan based on real-time observations. Recently, prompting Large Language Models (LLMs) to generate actions iteratively has become a prevalent paradigm due... | TREE-PLANNER: EFFICIENT CLOSE-LOOP TASK PLANNING WITH LARGE LANGUAGE MODELS |
d263310331 | We introduce Würstchen, a novel architecture for text-to-image synthesis that combines competitive performance with unprecedented cost-effectiveness for largescale text-to-image diffusion models. A key contribution of our work is to develop a latent diffusion technique in which we learn a detailed but extremely compact... | WÜRSTCHEN: AN EFFICIENT ARCHITECTURE FOR LARGE-SCALE TEXT-TO-IMAGE DIFFUSION MODELS |
d256358781 | We propose a continuous optimization framework for discovering a latent directed acyclic graph (DAG) from observational data.Our approach optimizes over the polytope of permutation vectors, the so-called Permutahedron, to learn a topological ordering.Edges can be optimized jointly, or learned conditional on the orderin... | DAG LEARNING ON THE PERMUTAHEDRON |
d252531622 | Time series classification is an important problem in real world. Due to its nonstationary property that the distribution changes over time, it remains challenging to build models for generalization to unseen distributions. In this paper, we propose to view time series classification from the distribution perspective. ... | OUT-OF-DISTRIBUTION REPRESENTATION LEARNING FOR TIME SERIES CLASSIFICATION |
d52135921 | Despite much effort, deep neural networks remain highly susceptible to tiny input perturbations and even for MNIST, one of the most common toy datasets in computer vision, no neural network model exists for which adversarial perturbations are large and make semantic sense to humans. We show that even the widely recogni... | TOWARDS THE FIRST ADVERSARIALLY ROBUST NEURAL NETWORK MODEL ON MNIST |
d201646591 | We make two theoretical contributions to disentanglement learning by (a) defining precise semantics of disentangled representations, and (b) establishing robust metrics for evaluation. First, we characterize the concept "disentangled representations" used in supervised and unsupervised methods along three dimensionsinf... | Theory and Evaluation Metrics for Learning Disentangled Representations |
d259252546 | Local motion blur commonly occurs in real-world photography due to the mixing between moving objects and stationary backgrounds during exposure.Existing image deblurring methods predominantly focus on global deblurring, inadvertently affecting the sharpness of backgrounds in locally blurred images and wasting unnecessa... | Adaptive Window Pruning for Efficient Local Motion Deblurring |
d108296442 | We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual concepts, words, and semantic parsing of sentences without explicit supervision on any of them; instead, our model learns by simply looking at images and reading paired questions and answers. Our model builds an object-based scene represe... | THE NEURO-SYMBOLIC CONCEPT LEARNER: INTERPRETING SCENES, WORDS, AND SENTENCES FROM NATURAL SUPERVISION |
d54477714 | Music relies heavily on repetition to build structure and meaning. Self-reference occurs on multiple timescales, from motifs to phrases to reusing of entire sections of music, such as in pieces with ABA structure. The Transformer(Vaswani et al., 2017), a sequence model based on self-attention, has achieved compelling r... | MUSIC TRANSFORMER: GENERATING MUSIC WITH LONG-TERM STRUCTURE |
d226226846 | Figure 1: The first column shows images generated by off-the-shelf 2D GANs trained on RGB images only, while the rest show that our method can unsupervisedly reconstruct 3D shape (viewed in 3D mesh, surface normal, and texture) given a single 2D image by exploiting the geometric clues contained in GANs. The last two co... | DO 2D GANS KNOW 3D SHAPE? UNSUPERVISED 3D SHAPE RECONSTRUCTION FROM 2D IMAGE GANS |
d57189211 | Object-based factorizations provide a useful level of abstraction for interacting with the world. Building explicit object representations, however, often requires supervisory signals that are difficult to obtain in practice. We present a paradigm for learning object-centric representations for physical scene understan... | REASONING ABOUT PHYSICAL INTERACTIONS WITH OBJECT-ORIENTED PREDICTION AND PLANNING |
d53082019 | We present a representation for describing transition models in complex uncertain domains using relational rules. For any action, a rule selects a set of relevant objects and computes a distribution over properties of just those objects in the resulting state given their properties in the previous state. An iterative g... | LEARNING SPARSE RELATIONAL TRANSITION MODELS |
d249625506 | The notion of neural collapse refers to several emergent phenomena that have been empirically observed across various canonical classification problems. During the terminal phase of training a deep neural network, the feature embedding of all examples of the same class tend to collapse to a single representation, and t... | Memorization-Dilation: Modeling Neural Collapse Under Noise |
d256274796 | Neural Architecture Search (NAS) is widely used to automatically obtain the neural network with the best performance among a large number of candidate architectures. To reduce the search time, zero-shot NAS aims at designing training-free proxies that can predict the test performance of a given architecture. However, a... | ZICO: ZERO-SHOT NAS VIA INVERSE COEFFICIENT OF VARIATION ON GRADIENTS |
d2703040 | Most existing neural networks for learning graphs address permutation invariance by conceiving of the network as a message passing scheme, where each node sums the feature vectors coming from its neighbors. We argue that this imposes a limitation on their representation power, and instead propose a new general architec... | COVARIANT COMPOSITIONAL NETWORKS FOR LEARNING GRAPHS |
d250334789 | Machine learning models are vulnerable to Out-Of-Distribution (OOD) examples, and such a problem has drawn much attention. However, current methods lack a full understanding of different types of OOD data: there are benign OOD data that can be properly adapted to enhance the learning performance, while other malign OOD... | HARNESSING OUT-OF-DISTRIBUTION EXAMPLES VIA AUGMENTING CONTENT AND STYLE |
d246634143 | Trainable layers such as convolutional building blocks are the standard network design choices by learning parameters to capture the global context through successive spatial operations. When designing an efficient network, trainable layers such as the depthwise convolution is the source of efficiency in the number of ... | LEARNING FEATURES WITH PARAMETER-FREE LAYERS |
d252070677 | Knowledge graphs (KGs) are known for their large scale and knowledge inference ability, but are also notorious for the incompleteness associated with them. Due to the long-tail distribution of the relations in KGs, few-shot KG completion has been proposed as a solution to alleviate incompleteness and expand the coverag... | Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion |
d54472058 | The Softmax function is used in the final layer of nearly all existing sequence-tosequence models for language generation. However, it is usually the slowest layer to compute which limits the vocabulary size to a subset of most frequent types; and it has a large memory footprint. We propose a general technique for repl... | VON MISES-FISHER LOSS FOR TRAINING SEQUENCE TO SEQUENCE MODELS WITH CONTINUOUS OUTPUTS |
d260497139 | In this paper, we present a systematic study on GANs with categorical discriminator, especially their impact on the optimization scheme of the generator. We derive class-aware gradients and cross-entropy decomposition, to theoretically reveal how they help GAN training and the inherent problems in previous models. Base... | Activation Maximization Generative Adversarial Nets |
d52911937 | The use of imitation learning to learn a single policy for a complex task that has multiple modes or hierarchical structure can be challenging. In fact, previous work has shown that when the modes are known, learning separate policies for each mode or sub-task can greatly improve the performance of imitation learning. ... | DIRECTED-INFO GAIL: LEARNING HIERARCHICAL POLICIES FROM UNSEGMENTED DEMONSTRATIONS USING DIRECTED INFORMATION |
d220404330 | In most real world scenarios, a policy trained by reinforcement learning in one environment needs to be deployed in another, potentially quite different environment. However, generalization across different environments is known to be hard. A natural solution would be to keep training after deployment in the new enviro... | Self-Supervised Policy Adaptation during Deployment |
d56895416 | We propose Stochastic Neural Architecture Search (SNAS), an economical endto-end solution to Neural Architecture Search (NAS) that trains neural operation parameters and architecture distribution parameters in same round of backpropagation, while maintaining the completeness and differentiability of the NAS pipeline. I... | SNAS: STOCHASTIC NEURAL ARCHITECTURE SEARCH |
d235390444 | Fair representation learning is an attractive approach that promises fairness of downstream predictors by encoding sensitive data. Unfortunately, recent work has shown that strong adversarial predictors can still exhibit unfairness by recovering sensitive attributes from these representations. In this work, we present ... | FAIR NORMALIZING FLOWS |
d222291521 | Given the input graph and its label/property, several key problems of graph learning, such as finding interpretable subgraphs, graph denoising and graph compression, can be attributed to the fundamental problem of recognizing a subgraph of the original one. This subgraph shall be as informative as possible, yet contain... | Graph Information Bottleneck for Subgraph Recognition |
d13352766 | While bigger and deeper neural network architectures continue to advance the state-of-the-art for many computer vision tasks, real-world adoption of these networks is impeded by hardware and speed constraints. Conventional model compression methods attempt to address this problem by modifying the architecture manually ... | N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning |
d222208633 | DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance. However, it suffers from slow convergence and limited feature spatial resolution, due to the limitation of Transformer attention modules in processing image feature maps. To ... | DEFORMABLE DETR: DEFORMABLE TRANSFORMERS FOR END-TO-END OBJECT DETECTION |
d252734897 | We introduce a new paradigm for generative modeling built on Continuous Normalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale. Specifically, we present the notion of Flow Matching (FM), a simulation-free approach for training CNFs based on regressing vector fields of fixed conditional probability p... | FLOW MATCHING FOR GENERATIVE MODELING |
d231847109 | A fundamental limitation of applying semi-supervised learning in real-world settings is the assumption that unlabeled test data contains only classes previously encountered in the labeled training data. However, this assumption rarely holds for data in-the-wild, where instances belonging to novel classes may appear at ... | OPEN-WORLD SEMI-SUPERVISED LEARNING |
d247084450 | This work targets automated designing and scaling of Vision Transformers (ViTs). The motivation comes from two pain spots: 1) the lack of efficient and principled methods for designing and scaling ViTs; 2) the tremendous computational cost of training ViT that is much heavier than its convolution counterpart. To tackle... | AUTO-SCALING VISION TRANSFORMERS WITHOUT TRAINING |
d68137503 | Variational Bayesian neural networks (BNNs) perform variational inference over weights, but it is difficult to specify meaningful priors and approximate posteriors in a high-dimensional weight space. We introduce functional variational Bayesian neural networks (fBNNs), which maximize an Evidence Lower BOund (ELBO) defi... | FUNCTIONAL VARIATIONAL BAYESIAN NEURAL NETWORKS |
d222140859 | Large-scale pre-trained language models such as BERT and RoBERTa have achieved state-of-the-art performance across a wide range of NLP tasks. Recent studies, however, show that such BERT-based models are vulnerable facing the threats of textual adversarial attacks. We aim to address this problem from an information-the... | INFOBERT: IMPROVING ROBUSTNESS OF LANGUAGE MODELS FROM AN INFORMATION THEORETIC PERSPECTIVE |
d231985449 | In this paper, we consider fully-connected feed-forward deep neural networks where weights and biases are independent and identically distributed according to Gaussian distributions. Extending previous results (Matthews et al., 2018a;b; Yang, 2019) we adopt a function-space perspective, i.e. we look at neural network... | LARGE-WIDTH FUNCTIONAL ASYMPTOTICS FOR DEEP GAUSSIAN NEURAL NETWORKS |
d235669672 | Self-supervised contrastive representation learning has proved incredibly successful in the vision and natural language domains, enabling state-of-the-art performance with orders of magnitude less labeled data. However, such methods are domainspecific and little has been done to leverage this technique on real-world ta... | SCARF: SELF-SUPERVISED CONTRASTIVE LEARNING USING RANDOM FEATURE CORRUPTION |
d256697539 | The real-time processing of time series signals is a critical issue for many reallife applications. The idea of real-time processing is especially important in audio domain as the human perception of sound is sensitive to any kind of disturbance in perceived signals, especially the lag between auditory and visual modal... | SHORT-TERM MEMORY CONVOLUTIONS |
d14307651 | The reparameterization trick enables optimizing large scale stochastic computation graphs via gradient descent. The essence of the trick is to refactor each stochastic node into a differentiable function of its parameters and a random variable with fixed distribution. After refactoring, the gradients of the loss propag... | THE CONCRETE DISTRIBUTION: A CONTINUOUS RELAXATION OF DISCRETE RANDOM VARIABLES |
d239049745 | In this paper, we perform an in-depth study of the properties and applications of aligned generative models. We refer to two models as aligned if they share the same architecture, and one of them (the child) is obtained from the other (the parent) via fine-tuning to another domain, a common practice in transfer learnin... | STYLEALIGN: ANALYSIS AND APPLICATIONS OF ALIGNED STYLEGAN MODELS |
d56657805 | Learning in environments with large state and action spaces, and sparse rewards, can hinder a Reinforcement Learning (RL) agent's learning through trial-anderror. For instance, following natural language instructions on the Web (such as booking a flight ticket) leads to RL settings where input vocabulary and number of ... | LEARNING TO NAVIGATE THE WEB |
d52948476 | It is well known that neural networks with rectified linear units (ReLU) activation functions are positively scale-invariant. Conventional algorithms like stochastic gradient descent optimize the neural networks in the vector space of weights, which is, however, not positively scale-invariant. This mismatch may lead to... | G-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space |
d252715693 | While recent camera-only 3D detection methods leverage multiple timesteps, the limited history they use significantly hampers the extent to which temporal fusion can improve object perception. Observing that existing works' fusion of multiframe images are instances of temporal stereo matching, we find that performance ... | TIME WILL TELL: NEW OUTLOOKS AND A BASELINE FOR TEMPORAL MULTI-VIEW 3D OBJECT DETECTION |
d227152280 | We demonstrate that differentially private machine learning has not yet reached its "AlexNet moment" on many canonical vision tasks: linear models trained on handcrafted features significantly outperform end-to-end deep neural networks for moderate privacy budgets. To exceed the performance of handcrafted features, we ... | Differentially Private Learning Needs Better Features (or Much More Data) |
d59604361 | Many real-world reinforcement learning tasks require multiple agents to make sequential decisions under the agents' interaction, where well-coordinated actions among the agents are crucial to achieve the target goal better at these tasks. One way to accelerate the coordination effect is to enable multiple agents to com... | LEARNING TO SCHEDULE COMMUNICATION IN MULTI-AGENT REINFORCEMENT LEARNING |
d52920808 | One of the mystery in the success of neural networks is randomly initialized first order methods like gradient descent can achieve zero training loss even though the objective function is non-convex and non-smooth. This paper demystifies this surprising phenomenon for two-layer fully connected ReLU activated neural net... | Gradient Descent Provably Optimizes Over-parameterized Neural Networks |
d5037032 | The current dominant paradigm for imitation learning relies on strong supervision of expert actions to learn both what and how to imitate.We pursue an alternative paradigm wherein an agent first explores the world without any expert supervision and then distills its experience into a goal-conditioned skill policy with ... | ZERO-SHOT VISUAL IMITATION |
d20038688 | Learning to learn is a powerful paradigm for enabling models to learn from data more effectively and efficiently. A popular approach to meta-learning is to train a recurrent model to read in a training dataset as input and output the parameters of a learned model, or output predictions for new test inputs. Alternativel... | META-LEARNING AND UNIVERSALITY: DEEP REPRESENTATIONS AND GRADIENT DESCENT CAN APPROXIMATE ANY LEARNING ALGORITHM |
d257365203 | Large-scale pre-trained multi-modal models (e.g., CLIP) demonstrate strong zeroshot transfer capability in many discriminative tasks, e.g., image classification. Their adaptation to zero-shot image-conditioned text generation tasks has drawn increasing interest. Prior arts approach to zero-shot captioning by either uti... | DECAP: DECODING CLIP LATENTS FOR ZERO-SHOT CAPTIONING VIA TEXT-ONLY TRAINING |
d232233563 | Reinforcement learning has been shown to be highly successful at many challenging tasks. However, success heavily relies on well-shaped rewards. Intrinsically motivated RL attempts to remove this constraint by defining an intrinsic reward function. Motivated by the self-consciousness concept in psychology, we make a na... | MUTUAL INFORMATION STATE INTRINSIC CONTROL |
d263829780 | Neural language models are probabilistic models of human text.They are predominantly trained using maximum likelihood estimation (MLE), which is equivalent to minimizing the forward cross-entropy between the empirical data distribution and the model distribution.However, various degeneration phenomena are still widely ... | EMO: EARTH MOVER DISTANCE OPTIMIZATION FOR AUTO-REGRESSIVE LANGUAGE MODELING |
d8696462 | Some machine learning applications involve training data that is sensitive, such as the medical histories of patients in a clinical trial. A model may inadvertently and implicitly store some of its training data; careful analysis of the model may therefore reveal sensitive information.To address this problem, we demons... | SEMI-SUPERVISED KNOWLEDGE TRANSFER FOR DEEP LEARNING FROM PRIVATE TRAINING DATA |
d211082893 | Generative models that can model and predict sequences of future events can, in principle, learn to capture complex real-world phenomena, such as physical interactions. However, a central challenge in video prediction is that the future is highly uncertain: a sequence of past observations of events can imply many possi... | VIDEOFLOW: A CONDITIONAL FLOW-BASED MODEL FOR STOCHASTIC VIDEO GENERATION |
d14048239 | Model-free deep reinforcement learning (RL) methods have been successful in a wide variety of simulated domains. However, a major obstacle facing deep RL in the real world is their high sample complexity. Batch policy gradient methods offer stable learning, but at the cost of high variance, which often requires large b... | Q-PROP: SAMPLE-EFFICIENT POLICY GRADIENT WITH AN OFF-POLICY CRITIC |
d51952942 | This paper studies a class of adaptive gradient based momentum algorithms that update the search directions and learning rates simultaneously using past gradients. This class, which we refer to as the "Adam-type", includes the popular algorithms such as the Adam [1], AMSGrad [2] and AdaGrad[3]. Despite their popularity... | On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization |
d244709059 | Many practical applications of reinforcement learning require agents to learn from sparse and delayed rewards. It challenges the ability of agents to attribute their actions to future outcomes. In this paper, we consider the problem formulation of episodic reinforcement learning with trajectory feedback. It refers to a... | LEARNING LONG-TERM REWARD REDISTRIBUTION VIA RANDOMIZED RETURN DECOMPOSITION |
d251253049 | Text-to-image models offer unprecedented freedom to guide creation through natural language. Yet, it is unclear how such freedom can be exercised to generate images of specific unique concepts, modify their appearance, or compose them in new roles and novel scenes. In other words, we ask: how can we use language-guided... | An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion |
d52947979 | Deep latent variable models have become a popular model choice due to the scalable learning algorithms introduced by(Kingma & Welling, 2013;Rezende et al., 2014). These approaches maximize a variational lower bound on the intractable log likelihood of the observed data.Burda et al. (2015)introduced a multi-sample vari... | DOUBLY REPARAMETERIZED GRADIENT ESTIMATORS FOR MONTE CARLO OBJECTIVES |
d262824542 | Teams that have trained large Transformer-based models have reported training instabilities at large scale that did not appear when training with the same hyperparameters at smaller scales.Although the causes of such instabilities are of scientific interest, the amount of resources required to reproduce them has made i... | Small-scale proxies for large-scale Transformer training instabilities |
d247475958 | Multi-omics data analysis has the potential to discover hidden molecular interactions, revealing potential regulatory and/or signal transduction pathways for cellular processes of interest when studying life and disease systems. One of critical challenges when dealing with real-world multi-omics data is that they may m... | MOREL: MULTI-OMICS RELATIONAL LEARNING |
d255186293 | Rigorous guarantees about the performance of predictive algorithms are necessary in order to ensure their responsible use. Previous work has largely focused on bounding the expected loss of a predictor, but this is not sufficient in many risk-sensitive applications where the distribution of errors is important. In this... | Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions |
d256503523 | Despite its outstanding performance in various graph tasks, vanilla Message Passing Neural Network (MPNN) usually fails in link prediction tasks, as it only uses representations of two individual target nodes and ignores the pairwise relation between them. To capture the pairwise relations, some models add manual featu... | Neural Common Neighbor with Completion for Link Prediction |
d227209335 | Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution... | SCORE-BASED GENERATIVE MODELING THROUGH STOCHASTIC DIFFERENTIAL EQUATIONS |
d224804162 | We consider the fundamental problem of how to automatically construct summary statistics for implicit generative models where the evaluation of likelihood function is intractable but sampling / simulating data from the model is possible. The idea is to frame the task of constructing sufficient statistics as learning mu... | NEURAL APPROXIMATE SUFFICIENT STATISTICS FOR IMPLICIT MODELS |
d232168939 | Human beings are able to understand objectives and learn by simply observing others perform a task. Imitation learning methods aim to replicate such capabilities, however, they generally depend on access to a full set of optimal states and actions taken with the agent's actuators and from the agent's point of view. In ... | DOMAIN-ROBUST VISUAL IMITATION LEARNING WITH MUTUAL INFORMATION CONSTRAINTS |
d221995507 | Convolutional image classifiers can achieve high predictive accuracy, but quantifying their uncertainty remains an unresolved challenge, hindering their deployment in consequential settings. Existing uncertainty quantification techniques, such as Platt scaling, attempt to calibrate the network's probability estimates, ... | UNCERTAINTY SETS FOR IMAGE CLASSIFIERS USING CONFORMAL PREDICTION |
d234357892 | Although tremendous strides have been made in uncontrolled face detection, efficient face detection with a low computation cost as well as high precision remains an open challenge. In this paper, we point out that training data sampling and computation distribution strategies are the keys to efficient and accurate face... | Sample and Computation Redistribution for Efficient Face Detection |
d252668508 | Individual privacy accounting enables bounding differential privacy (DP) loss individually for each participant involved in the analysis. This can be informative as often the individual privacy losses are considerably smaller than those indicated by the DP bounds that are based on considering worst-case bounds at each ... | Individual Privacy Accounting with Gaussian Differential Privacy |
d246430621 | We introduce the first metric for evaluating disentanglement at individual hierarchy levels of a structured latent representation. Applied to object-centric generative models, this offers a systematic, unified approach to evaluating (i) object separation between latent slots (ii) disentanglement of object properties in... | EVALUATING DISENTANGLEMENT OF STRUCTURED REPRESENTATIONS |
d59608630 | The ability to generalize quickly from few observations is crucial for intelligent systems. In this paper we introduce APL, an algorithm that approximates probability distributions by remembering the most surprising observations it has encountered. These past observations are recalled from an external memory module and... | ADAPTIVE POSTERIOR LEARNING: FEW-SHOT LEARNING WITH A SURPRISE-BASED MEMORY MODULE |
d263605885 | Despite the remarkable advances in language modeling, current mainstream decoding methods still struggle to generate texts that align with human texts across different aspects.In particular, sampling-based methods produce less-repetitive texts which are often disjunctive in discourse, while search-based methods maintai... | LANGUAGE MODEL DECODING AS DIRECT METRICS OPTIMIZATION |
d257364792 | 3D convolution neural networks (CNNs) have been the prevailing option for video recognition. To capture the temporal information, 3D convolutions are computed along the sequences, leading to cubically growing and expensive computations. To reduce the computational cost, previous methods resort to manually designed 3D/2... | MAXIMIZING SPATIO-TEMPORAL ENTROPY OF DEEP 3D CNNS FOR EFFICIENT VIDEO RECOGNITION |
d259243562 | We study the cost of overfitting in noisy kernel ridge regression (KRR), which we define as the ratio between the test error of the interpolating ridgeless model and the test error of the optimally-tuned model. We take an "agnostic" view in the following sense: we consider the cost as a function of sample size for any ... | An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression |
d263608822 | Retrieval-augmented language models (RALMs) hold promise to produce language understanding systems that are are factual, efficient, and up-to-date.An important desideratum of RALMs, is that retrieved information helps model performance when it is relevant, and does not harm performance when it is not.This is particular... | MAKING RETRIEVAL-AUGMENTED LANGUAGE MODELS ROBUST TO IRRELEVANT CONTEXT |
d261530996 | In this work, we study first-order algorithms for solving Bilevel Optimization (BO) where the objective functions are smooth but possibly nonconvex in both levels and the variables are restricted to closed convex sets. As a first step, we study the landscape of BO through the lens of penalty methods, in which the upper... | On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation |
d257766959 | Imagining the future trajectory is the key for robots to make sound planning and successfully reach their goals. Therefore, text-conditioned video prediction (TVP) is an essential task to facilitate general robot policy learning, i.e., predicting future video frames with a given language instruction and reference frame... | Seer: Language Instructed Video Prediction with Latent Diffusion Models |
d257378635 | This paper presents a multi-agent reinforcement learning (MARL) scheme for proactive Multi-Camera Collaboration in 3D Human Pose Estimation in dynamic human crowds. Traditional fixed-viewpoint multi-camera solutions for human motion capture (MoCap) are limited in capture space and susceptible to dynamic occlusions. Act... | PROACTIVE MULTI-CAMERA COLLABORATION FOR 3D HUMAN POSE ESTIMATION |
d257254919 | Recent methods for imitation learning directly learn a Q-function using an implicit reward formulation rather than an explicit reward function. However, these methods generally require implicit reward regularization to improve stability and often mistreat absorbing states. Previous works show that a squared norm regula... | LS-IQ: IMPLICIT REWARD REGULARIZATION FOR INVERSE REINFORCEMENT LEARNING |
d208310100 | A structured understanding of our world in terms of objects, relations, and hierarchies is an important component of human cognition. Learning such a structured world model from raw sensory data remains a challenge. As a step towards this goal, we introduce Contrastively-trained Structured World Models (C-SWMs). C-SWMs... | CONTRASTIVE LEARNING OF STRUCTURED WORLD MODELS |
d256104917 | There is an emerging trend of using neural implicit functions for map representation in Simultaneous Localization and Mapping (SLAM). Some pioneer works have achieved encouraging results on RGB-D SLAM. In this paper, we present a dense RGB SLAM method with neural implicit map representation. To reach this challenging g... | DENSE RGB SLAM WITH NEURAL IMPLICIT MAPS |
d3313632 | Adversarial perturbations of normal images are usually imperceptible to humans, but they can seriously confuse state-of-the-art machine learning models. What makes them so special in the eyes of image classifiers? In this paper, we show empirically that adversarial examples mainly lie in the low probability regions of ... | PIXELDEFEND: LEVERAGING GENERATIVE MODELS TO UNDERSTAND AND DEFEND AGAINST ADVERSARIAL EXAMPLES |
d252595707 | We study the problem of training a two-layer neural network (NN) of arbitrary width using stochastic gradient descent (SGD) where the input x ∈ R d is Gaussian and the target y ∈ R follows a multiple-index model, i.e., y = g( u1, x , . . . , uk, x ) with a noisy link function g. We prove that the first-layer weights of... | Neural Networks Efficiently Learn Low-Dimensional Representations with SGD |
d235458498 | Low-precision arithmetic trains deep learning models using less energy, less memory and less time. However, we pay a price for the savings: lower precision may yield larger round-off error and hence larger prediction error. As applications proliferate, users must choose which precision to use to train a new model, and ... | HOW LOW CAN WE GO: TRADING MEMORY FOR ERROR IN LOW-PRECISION TRAINING |
d210861275 | In contrast to fully connected networks, Convolutional Neural Networks (CNNs) achieve efficiency by learning weights associated with local filters with a finite spatial extent. An implication of this is that a filter may know what it is looking at, but not where it is positioned in the image. Information concerning abs... | HOW MUCH POSITION INFORMATION DO CONVOLUTIONAL NEURAL NETWORKS ENCODE? |
d1638605 | Although deep learning models have proven effective at solving problems in natural language processing, the mechanism by which they come to their conclusions is often unclear. As a result, these models are generally treated as black boxes, yielding no insight of the underlying learned patterns. In this paper we conside... | AUTOMATIC RULE EXTRACTION FROM LONG SHORT TERM MEMORY NETWORKS |
d208138363 | We present a provable, sampling-based approach for generating compact Convolutional Neural Networks (CNNs) by identifying and removing redundant filters from an over-parameterized network. Our algorithm uses a small batch of input data points to assign a saliency score to each filter and constructs an importance sampli... | PROVABLE FILTER PRUNING FOR EFFICIENT NEURAL NETWORKS |
d257078711 | A recourse action aims to explain a particular algorithmic decision by showing one specific way in which the instance could be modified to receive an alternate outcome. Existing recourse generation methods often assume that the machine learning model does not change over time. However, this assumption does not always h... | DISTRIBUTIONALLY ROBUST RECOURSE ACTION |
d53867751 | We study the emergence of cooperative behaviors in reinforcement learning agents by introducing a challenging competitive multi-agent soccer environment with continuous simulated physics. We demonstrate that decentralized, populationbased training with co-play can lead to a progression in agents' behaviors: from random... | EMERGENT COORDINATION THROUGH COMPETITION |
d252280353 | Time series classification is an important problem in real world. Due to its nonstationary property that the distribution changes over time, it remains challenging to build models for generalization to unseen distributions. In this paper, we propose to view time series classification from the distribution perspective. ... | OUT-OF-DISTRIBUTION REPRESENTATION LEARNING FOR TIME SERIES CLASSIFICATION |
d261697072 | The Mixture of Experts (MoE) is a widely known neural architecture where an ensemble of specialized sub-models optimizes overall performance with a constant computational cost. However, conventional MoEs pose challenges at scale due to the need to store all experts in memory. In this paper, we push MoE to the limit. We... | Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning |
d208202099 | Parallel developments in neuroscience and deep learning have led to mutually productive exchanges, pushing our understanding of real and artificial neural networks in sensory and cognitive systems. However, this interaction between fields is less developed in the study of motor control. In this work, we develop a virtu... | DEEP NEUROETHOLOGY OF A VIRTUAL RODENT |
d245634209 | We study the ability of foundation models to learn representations for classification that are transferable to new, unseen classes. Recent results in the literature show that representations learned by a single classifier over many classes are competitive on few-shot learning problems with representations learned by sp... | ON THE ROLE OF NEURAL COLLAPSE IN TRANSFER LEARNING |
d263835408 | Content warning: This paper contains examples of harmful language.The rapid progress in open-source large language models (LLMs) is significantly advancing AI development. Extensive efforts have been made before model release to align their behavior with human values, with the primary goal of ensuring their helpfulness... | CATASTROPHIC JAILBREAK OF OPEN-SOURCE LLMS VIA EXPLOITING GENERATION |
d236034070 | We study the problem of inferring an object-centric scene representation from a single image, aiming to derive a representation that is learned without supervision, explains the image formation process, and captures the scene's 3D nature. Most existing methods on scene decomposition lack one or more of these characteri... | UNSUPERVISED DISCOVERY OF OBJECT RADIANCE FIELDS |
d262944419 | Recent advances in Language Model (LM) agents and tool use, exemplified by applications like ChatGPT Plugins, enable a rich set of capabilities but also amplify potential risks-such as leaking private data or causing financial losses. Identifying these risks is labor-intensive, necessitating implementing the tools, set... | Identifying the Risks of LM Agents with an LM-Emulated Sandbox |
d219965999 | A key challenge for reinforcement learning (RL) consists of learning in environments with sparse extrinsic rewards. In contrast to current RL methods, humans are able to learn new skills with little or no reward by using various forms of intrinsic motivation. We propose AMIGO, a novel agent incorporating a goalgenerati... | Learning with AMIGO: Adversarially Motivated Intrinsic Goals |
d254591805 | Recent advances in generative adversarial networks (GANs) have demonstrated the capabilities of generating stunning photo-realistic portrait images. While some prior works have applied such image GANs to unconditional 2D portrait video generation and static 3D portrait synthesis, there are few works successfully extend... | PV3D: A 3D GENERATIVE MODEL FOR PORTRAIT VIDEO GENERATION |
d1755439 | Adversarial neural networks solve many important problems in data science, but are notoriously difficult to train. These difficulties come from the fact that optimal weights for adversarial nets correspond to saddle points, and not minimizers, of the loss function. The alternating stochastic gradient methods typically ... | Stabilizing Adversarial Nets With Prediction Methods |
d263609175 | There is a long history, as well as a recent explosion of interest, in statistical and generative modeling approaches based on score functions -derivatives of the log-likelihood of a distribution. In seminal works, Hyvärinen proposed vanilla score matching as a way to learn distributions from data by computing an estim... | Sampling Multimodal Distributions with the Vanilla Score: Benefits of Data-Based Initialization |
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