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d259108957 | Distributed and federated learning algorithms and techniques associated primarily with minimization problems. However, with the increase of minimax optimization and variational inequality problems in machine learning, the necessity of designing efficient distributed/federated learning approaches for these problems is b... | Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates |
d7289949 | Learning tasks such as those involving genomic data often poses a serious challenge: the number of input features can be orders of magnitude larger than the number of training examples, making it difficult to avoid overfitting, even when using the known regularization techniques. We focus here on tasks in which the inp... | DIET NETWORKS: THIN PARAMETERS FOR FAT GENOMICS |
d229212743 | Over the past years, deep generative models have achieved a new level of performance.Generated data has become difficult, if not impossible, to be distinguished from real data.While there are plenty of use cases that benefit from this technology, there are also strong concerns on how this new technology can be misused ... | RESPONSIBLE DISCLOSURE OF GENERATIVE MODELS USING SCALABLE FINGERPRINTING |
d257255328 | Neural Radiance Fields (NeRFs) aim to synthesize novel views of objects and scenes, given the object-centric camera views with large overlaps. However, we conjugate that this paradigm does not fit the nature of the street views that are collected by many self-driving cars from the large-scale unbounded scenes. Also, th... | S-NERF: NEURAL RADIANCE FIELDS FOR STREET VIEWS |
d256615829 | Adversarial attack serves as a major challenge for neural network models in NLP, which precludes the model's deployment in safety-critical applications. A recent line of work, detection-based defense, aims to distinguish adversarial sentences from benign ones. However, the core limitation of previous detection methods ... | TEXTSHIELD: BEYOND SUCCESSFULLY DETECTING ADVERSARIAL SENTENCES IN TEXT CLASSIFICATION |
d263605591 | Amazon products Input Views Novel Views Input Views Novel Views Input Views Novel ViewsFigure 1: LEAP performs 3D modeling from sparse views without camera pose information.We show the capability of LEAP on real-world cases with three unposed image inputs.We show one of the inputs. | LEAP: LIBERATE SPARSE-VIEW 3D MODELING FROM CAMERA POSES |
d260334705 | Can we better anticipate an actor's future actions (e.g.mix eggs) by knowing what commonly happens after the current action (e.g.crack eggs)?What if the actor also shares the goal (e.g.make fried rice) with us?The long-term action anticipation (LTA) task aims to predict an actor's future behavior from video observation... | AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos? |
d219965949 | In this paper we analyse and improve integer discrete flows for lossless compression. Integer discrete flows are a recently proposed class of models that learn invertible transformations for integer-valued random variables. Due to its discrete nature, they can be combined in a straightforward manner with entropy coding... | IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression |
d57375714 | Recent improvements to Generative Adversarial Networks (GANs) have made it possible to generate realistic images in high resolution based on natural language descriptions such as image captions. However, fine-grained control of the image layout, i.e. where in the image specific objects should be located, is still diffi... | GENERATING MULTIPLE OBJECTS AT SPATIALLY DISTINCT LOCATIONS |
d233481716 | Weakly supervised segmentation requires assigning a label to every pixel based on training instances with partial annotations such as image-level tags, object bounding boxes, labeled points and scribbles. This task is challenging, as coarse annotations (tags, boxes) lack precise pixel localization whereas sparse annota... | UNIVERSAL WEAKLY SUPERVISED SEGMENTATION BY PIXEL-TO-SEGMENT CONTRASTIVE LEARNING |
d12200521 | We propose zoneout, a novel method for regularizing RNNs. At each timestep, zoneout stochastically forces some hidden units to maintain their previous values. Like dropout, zoneout uses random noise to train a pseudo-ensemble, improving generalization. But by preserving instead of dropping hidden units, gradient inform... | Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations |
d249954054 | Large language models distill broad knowledge from text corpora. However, they can be inconsistent when it comes to completing user specified tasks. This issue can be addressed by finetuning such models via supervised learning on curated datasets, or via reinforcement learning. In this work, we propose a novel offline ... | OFFLINE RL FOR NATURAL LANGUAGE GENERATION WITH IMPLICIT LANGUAGE Q LEARNING |
d11480374 | This paper introduces a new neural structure called FusionNet, which extends existing attention approaches from three perspectives. First, it puts forward a novel concept of "history of word" to characterize attention information from the lowest word-level embedding up to the highest semantic-level representation. Seco... | FUSIONNET: FUSING VIA FULLY-AWARE ATTENTION WITH APPLICATION TO MACHINE COMPREHENSION |
d16367617 | Generative adversarial networks (GANs) are a framework for producing a generative model by way of a two-player minimax game. In this paper, we propose the Generative Multi-Adversarial Network (GMAN), a framework that extends GANs to multiple discriminators. In previous work, the successful training of GANs requires mod... | GENERATIVE MULTI-ADVERSARIAL NETWORKS |
d9651443 | We present a novel framework for generating pop music. Our model is a hierarchical Recurrent Neural Network, where the layers and the structure of the hierarchy encode our prior knowledge about how pop music is composed. In particular, the bottom layers generate the melody, while the higher levels produce the drums and... | SONG FROM PI: A MUSICALLY PLAUSIBLE NETWORK FOR POP MUSIC GENERATION |
d851777 | We propose Edward, a Turing-complete probabilistic programming language. Edward defines two compositional representations-random variables and inference. By treating inference as a first class citizen, on a par with modeling, we show that probabilistic programming can be as flexible and computationally efficient as tra... | DEEP PROBABILISTIC PROGRAMMING |
d247158860 | Evaluation metrics in machine learning are often hardly taken as loss functions, as they could be non-differentiable and non-decomposable, e.g., average precision and F1 score. This paper aims to address this problem by revisiting the surrogate loss learning, where a deep neural network is employed to approximate the e... | RELATIONAL SURROGATE LOSS LEARNING |
d53576131 | Although deep convolutional networks have achieved improved performance in many natural language tasks, they have been treated as black boxes because they are difficult to interpret. Especially, little is known about how they represent language in their intermediate layers.In an attempt to understand the representation... | DISCOVERY OF NATURAL LANGUAGE CONCEPTS IN INDIVIDUAL UNITS OF CNNS |
d248721740 | We address the task of view synthesis, generating novel views of a scene given a set of images as input. In many recent works such as NeRF (Mildenhall et al., 2020), the scene geometry is parameterized using neural implicit representations (i.e., MLPs). Implicit neural representations have achieved impressive visual q... | VIEW SYNTHESIS WITH SCULPTED NEURAL POINTS |
d222208920 | Autoregressive language models pretrained on large corpora have been successful at solving downstream tasks, even with zero-shot usage. However, there is little theoretical justification for their success. This paper considers the following questions: (1) Why should learning the distribution of natural language help wi... | A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks |
d258180514 | We study dynamic algorithms robust to adaptive input generated from sources with bounded capabilities, such as sparsity or limited interaction. For example, we consider robust linear algebraic algorithms when the updates to the input are sparse but given by an adversary with access to a query oracle. We also study robu... | Robust Algorithms on Adaptive Inputs from Bounded Adversaries |
d219636236 | Both uncertainty estimation and interpretability are important factors for trustworthy machine learning systems. However, there is little work at the intersection of these two areas. We address this gap by proposing a novel method for interpreting uncertainty estimates from differentiable probabilistic models, like Bay... | Getting a CLUE: A Method for Explaining Uncertainty Estimates |
d248376815 | Anytime inference requires a model to make a progression of predictions which might be halted at any time. Prior research on anytime visual recognition has mostly focused on image classification. We propose the first unified and end-toend approach for anytime dense prediction. A cascade of "exits" is attached to the mo... | ANYTIME DENSE PREDICTION WITH CONFIDENCE ADAPTIVITY |
d228373733 | Model-Predictive Control (MPC) is a powerful tool for controlling complex, real-world systems that uses a model to make predictions about future behavior. For each state encountered, MPC solves an online optimization problem to choose a control action that will minimize future cost. This is a surprisingly effective str... | BLENDING MPC & VALUE FUNCTION APPROXIMATION FOR EFFICIENT REINFORCEMENT LEARNING |
d210860781 | Autoencoder-based learning has emerged as a staple for disciplining representations in unsupervised and semi-supervised settings.This paper analyzes a framework for improving generalization in a purely supervised setting, where the target space is high-dimensional.We motivate and formalize the general framework of targ... | TARGET-EMBEDDING AUTOENCODERS FOR SUPERVISED REPRESENTATION LEARNING |
d249953817 | The ability to predict future visual observations conditioned on past observations and motor commands can enable embodied agents to plan solutions to a variety of tasks in complex environments. This work shows that we can create good video prediction models by pre-training transformers via masked visual modeling. Our a... | MaskViT: Masked Visual Pre-Training for Video Prediction |
d264172935 | We are interested in enabling visual planning for complex long-horizon tasks in the space of generated videos and language, leveraging recent advances in large generative models pretrained on Internet-scale data. To this end, we present video language planning (VLP), an algorithm that consists of a tree search procedur... | VIDEO LANGUAGE PLANNING |
d20140417 | We build on auto-encoding sequential Monte Carlo (AESMC): 1 a method for model and proposal learning based on maximizing the lower bound to the log marginal likelihood in a broad family of structured probabilistic models. Our approach relies on the efficiency of sequential Monte Carlo (SMC) for performing inference in ... | AUTO-ENCODING SEQUENTIAL MONTE CARLO |
d221508448 | Irregular sampling occurs in many time series modeling applications where it presents a significant challenge to standard deep learning models. This work is motivated by the analysis of physiological time series data in electronic health records, which are sparse, irregularly sampled, and multivariate. In this paper, w... | MULTI-TIME ATTENTION NETWORKS FOR IRREGULARLY SAMPLED TIME SERIES |
d257038864 | Off-policy learning ability is an important feature of reinforcement learning (RL) for practical applications. However, even one of the most elementary RL algorithms, temporal-difference (TD) learning, is known to suffer form divergence issue when the off-policy scheme is used together with linear function approximatio... | BACKSTEPPING TEMPORAL DIFFERENCE LEARNING |
d238856821 | Existing approaches to lifelong language learning rely on plenty of labeled data for learning a new task, which is hard to obtain in most real scenarios. Considering that humans can continually learn new tasks from a handful of examples, we expect the models also to be able to generalize well on new few-shot tasks with... | LFPT5: A UNIFIED FRAMEWORK FOR LIFELONG FEW-SHOT LANGUAGE LEARNING BASED ON PROMPT TUNING OF T5 |
d220042361 | A major challenge in modern reinforcement learning (RL) is efficient control of dynamical systems from high-dimensional sensory observations. Learning controllable embedding (LCE) is a promising approach that addresses this challenge by embedding the observations into a lower-dimensional latent space, estimating the la... | Control-Aware Representations for Model-based Reinforcement Learning |
d264590171 | Masked Image Modeling (MIM) is a powerful self-supervised strategy for visual pre-training without the use of labels.MIM applies random crops to input images, processes them with an encoder, and then recovers the masked inputs with a decoder, which encourages the network to capture and learn structural information abou... | PRE-TRAINING WITH RANDOM ORTHOGONAL PROJECTION IMAGE MODELING |
d252907467 | Machine learning applications frequently come with multiple diverse objectives and constraints that can change over time. Accordingly, trained models can be tuned with sets of hyper-parameters that affect their predictive behavior (e.g., their run-time efficiency versus error rate). As the number of constraints and hyp... | EFFICIENTLY CONTROLLING MULTIPLE RISKS WITH PARETO TESTING |
d202121359 | Policy gradient methods with actor-critic schemes demonstrate tremendous empirical successes, especially when the actors and critics are parameterized by neural networks. However, it remains less clear whether such "neural" policy gradient methods converge to globally optimal policies and whether they even converge at ... | Neural Policy Gradient Methods: Global Optimality and Rates of Convergence |
d258865836 | We present a novel approach to adapting pre-trained large language models (LLMs) to perform question answering (QA) and speech continuation.By endowing the LLM with a pre-trained speech encoder, our model becomes able to take speech inputs and generate speech outputs.The entire system is trained end-to-end and operates... | Spoken Question Answering and Speech Continuation Using Spectrogram-Powered LLM |
d252967732 | Deep Graph Networks (DGNs) currently dominate the research landscape of learning from graphs, due to their efficiency and ability to implement an adaptive message-passing scheme between the nodes. However, DGNs are typically limited in their ability to propagate and preserve long-term dependencies between nodes, i.e., ... | ANTI-SYMMETRIC DGN: A STABLE ARCHITECTURE FOR DEEP GRAPH NETWORKS |
d250144675 | Data augmentation has recently emerged as an essential component of modern training recipes for visual recognition tasks. However, data augmentation for video recognition has been rarely explored despite its effectiveness. Few existing augmentation recipes for video recognition naively extend the image augmentation met... | Exploring Temporally Dynamic Data Augmentation for Video Recognition |
d3331622 | Our work addresses two important issues with recurrent neural networks: (1) they are over-parameterized, and (2) the recurrence matrix is ill-conditioned. The former increases the sample complexity of learning and the training time. The latter causes the vanishing and exploding gradient problem. We present a flexible r... | Kronecker Recurrent Units |
d232046055 | Deep reinforcement learning primarily focuses on learning behavior, usually overlooking the fact that an agent's function is largely determined by form. So, how should one go about finding a morphology fit for solving tasks in a given environment? Current approaches that co-adapt morphology and behavior use a specific ... | TASK-AGNOSTIC MORPHOLOGY EVOLUTION |
d51928102 | When generating adversarial examples to attack deep neural networks (DNNs), p norm of the added perturbation is usually used to measure the similarity between original image and adversarial example. However, such adversarial attacks perturbing the raw input spaces may fail to capture structural information hidden in th... | STRUCTURED ADVERSARIAL ATTACK: TOWARDS GENERAL IMPLEMENTATION AND BETTER INTERPRETABILITY |
d257102638 | When deployed for risk-sensitive tasks, deep neural networks must include an uncertainty estimation mechanism. Here we examine the relationship between deep architectures and their respective training regimes, with their corresponding selective prediction and uncertainty estimation performance. We consider some of the ... | WHAT CAN WE LEARN FROM THE SELECTIVE PREDICTION AND UNCERTAINTY ESTIMATION PERFORMANCE OF 523 IMAGENET CLASSIFIERS? |
d244130249 | The vulnerability of machine learning models to membership inference attacks has received much attention in recent years. Existing attacks mostly remain impractical due to having high false positive rates, where non-member samples are often erroneously predicted as members. This type of error makes the predicted member... | ON THE IMPORTANCE OF DIFFICULTY CALIBRATION IN MEMBERSHIP INFERENCE ATTACKS |
d14911774 | The current mainstream approach to train natural language systems is to expose them to large amounts of text. This passive learning is problematic if we are interested in developing interactive machines, such as conversational agents. We propose a framework for language learning that relies on multi-agent communication... | MULTI-AGENT COOPERATION AND THE EMERGENCE OF (NATURAL) LANGUAGE |
d228063969 | Neural networks have shown tremendous potential for reconstructing highresolution images in inverse problems. The non-convex and opaque nature of neural networks, however, hinders their utility in sensitive applications such as medical imaging. To cope with this challenge, this paper advocates a convex duality framewor... | CONVEX REGULARIZATION BEHIND NEURAL RECONSTRUCTION |
d258331993 | Video is a promising source of knowledge for embodied agents to learn models of the world's dynamics. Large deep networks have become increasingly effective at modeling complex video data in a self-supervised manner, as evaluated by metrics based on human perceptual similarity or pixel-wise comparison. However, it rema... | A CONTROL-CENTRIC BENCHMARK FOR VIDEO PREDICTION |
d259075723 | Malicious server (MS) attacks have enabled the scaling of data stealing in federated learning to large batch sizes and secure aggregation, settings previously considered private. However, many concerns regarding client-side detectability of MS attacks were raised, questioning their practicality once they are publicly k... | Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning |
d231934149 | Graph neural networks (GNNs) are a powerful architecture for tackling graph learning tasks, yet have been shown to be oblivious to eminent substructures such as cycles. We present TOGL, a novel layer that incorporates global topological information of a graph using persistent homology. TOGL can be easily integrated int... | TOPOLOGICAL GRAPH NEURAL NETWORKS |
d238583483 | This work studies the question of Representation Learning in RL: how can we learn a compact low-dimensional representation such that on top of the representation we can perform RL procedures such as exploration and exploitation, in a sample efficient manner. We focus on the low-rank Markov Decision Processes (MDPs) whe... | Representation Learning for Online and Offline RL in Low-rank MDPs |
d53216818 | We propose a "plan online and learn offline" framework for the setting where an agent, with an internal model, needs to continually act and learn in the world. Our work builds on the synergistic relationship between local model-based control, global value function learning, and exploration. We study how local trajector... | Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control |
d257557557 | While significant research progress has been made in robot learning for control, unique challenges arise when simultaneously co-optimizing morphology. Existing work has typically been tailored for particular environments or representations. In order to more fully understand inherent design and performance tradeoffs and... | SOFTZOO: A SOFT ROBOT CO-DESIGN BENCHMARK FOR LOCOMOTION IN DIVERSE ENVIRONMENTS |
d238857090 | Recent methods for embodied instruction following are typically trained end-toend using imitation learning. This often requires the use of expert trajectories and low-level language instructions. Such approaches assume that neural states will integrate multimodal semantics to perform state tracking, building spatial me... | FILM: FOLLOWING INSTRUCTIONS IN LANGUAGE WITH MODULAR METHODS |
d247244493 | Better-supervised models might have better performance. In this paper, we first clarify what makes for good supervision for a classification problem, and then explain two existing label refining methods, label smoothing and knowledge distillation, in terms of our proposed criterion. To further answer why and how better... | BETTER SUPERVISORY SIGNALS BY OBSERVING LEARNING PATHS |
d219558836 | Fine-tuning pre-trained transformer-based language models such as BERT has become a common practice dominating leaderboards across various NLP benchmarks. Despite the strong empirical performance of fine-tuned models, fine-tuning is an unstable process: training the same model with multiple random seeds can result in a... | On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines |
d17711681 | Maximum entropy modeling is a flexible and popular framework for formulating statistical models given partial knowledge. In this paper, rather than the traditional method of optimizing over the continuous density directly, we learn a smooth and invertible transformation that maps a simple distribution to the desired ma... | MAXIMUM ENTROPY FLOW NETWORKS |
d247450890 | Periodic time series (PTS) forecasting plays a crucial role in a variety of industries to foster critical tasks, such as early warning, pre-planning, resource scheduling, etc. However, the complicated dependencies of the PTS signal on its inherent periodicity as well as the sophisticated composition of various periods ... | DEPTS: DEEP EXPANSION LEARNING FOR PERIODIC TIME SERIES FORECASTING |
d252681067 | Procedural planning aims to implement complex high-level goals by decomposition into sequential simpler low-level steps. Although procedural planning is a basic skill set for humans in daily life, it remains a challenge for large language models (LLMs) that lack a deep understanding of the cause-effect relations in pro... | NEURO-SYMBOLIC PROCEDURAL PLANNING WITH COMMONSENSE PROMPTING |
d201646137 | In seeking for sparse and efficient neural network models, many previous works investigated on enforcing 1 or 0 regularizers to encourage weight sparsity during training. The 0 regularizer measures the parameter sparsity directly and is invariant to the scaling of parameter values. But it cannot provide useful gradient... | DEEPHOYER: LEARNING SPARSER NEURAL NETWORK WITH DIFFERENTIABLE SCALE-INVARIANT SPARSITY MEASURES |
d202888483 | A leading hypothesis for the surprising generalization of neural networks is that the dynamics of gradient descent bias the model towards simple solutions, by searching through the solution space in an incremental order of complexity. We formally define the notion of incremental learning dynamics and derive the conditi... | THE IMPLICIT BIAS OF DEPTH: HOW INCREMENTAL LEARNING DRIVES GENERALIZATION |
d256105245 | Heterogeneity of data distributed across clients limits the performance of global models trained through federated learning, especially in the settings with highly imbalanced class distributions of local datasets. In recent years, personalized federated learning (pFL) has emerged as a potential solution to the challeng... | THE BEST OF BOTH WORLDS: ACCURATE GLOBAL AND PERSONALIZED MODELS THROUGH FEDERATED LEARNING WITH DATA-FREE HYPER-KNOWLEDGE DISTILLATION |
d252089864 | Federated learning (FL) is a subfield of machine learning where multiple clients try to collaboratively learn a model over a network under communication constraints. We consider finite-sum federated optimization under a second-order function similarity condition and strong convexity, and propose two new algorithms: SVR... | Faster federated optimization under second-order similarity |
d256389917 | Real-world deployment of machine learning models is challenging because data evolves over time.While no model can work when data evolves in an arbitrary fashion, if there is some pattern to these changes, we might be able to design methods to address it.This paper addresses situations when data evolves gradually.We int... | TIME-VARYING PROPENSITY SCORE TO BRIDGE THE GAP BETWEEN THE PAST AND PRESENT |
d199528271 | The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization algorithms like RMSprop and Adam. Pursuing the theory behind warmup, we identify a problem of the adaptive learning rate -its variance is pro... | ON THE VARIANCE OF THE ADAPTIVE LEARNING RATE AND BEYOND |
d238353966 | Randomized ensembled double Q-learning (REDQ) (Chen et al., 2021b) has recently achieved state-of-the-art sample efficiency on continuous-action reinforcement learning benchmarks. This superior sample efficiency is made possible by using a large Q-function ensemble. However, REDQ is much less computationally efficient ... | DROPOUT Q-FUNCTIONS FOR DOUBLY EFFICIENT REINFORCEMENT LEARNING |
d260900008 | Recent progress in large language models (LLMs) like GPT-4 and PaLM-2 has brought significant advancements in addressing math reasoning problems. In particular, OpenAI's latest version of GPT-4, known as GPT-4 Code Interpreter, shows remarkable performance on challenging math datasets. In this paper, we explore the eff... | SOLVING CHALLENGING MATH WORD PROBLEMS USING GPT-4 CODE INTERPRETER WITH CODE-BASED SELF-VERIFICATION |
d202539918 | Many advances in Natural Language Processing have been based upon more expressive models for how inputs interact with the context in which they occur. Recurrent networks, which have enjoyed a modicum of success, still lack the generalization and systematicity ultimately required for modelling language. In this work, we... | Mogrifier LSTM |
d221640669 | Denoising score matching with Annealed Langevin Sampling (DSM-ALS) is a recent approach to generative modeling. Despite the convincing visual quality of samples, this method appears to perform worse than Generative Adversarial Networks (GANs) under the Fréchet Inception Distance, a popular metric for generative models.... | ADVERSARIAL SCORE MATCHING AND IMPROVED SAMPLING FOR IMAGE GENERATION |
d263608898 | The effect of underrepresentation on the performance of minority groups is known to be a serious problem in supervised learning settings; however, it has been underexplored so far in the context of self-supervised learning (SSL).In this paper, we demonstrate that contrastive learning (CL), a popular variant of SSL, ten... | An Investigation of Representation and Allocation Harms in Contrastive Learning |
d259076379 | Recent advances in large language model (LLM) pretraining have led to highquality LLMs with impressive abilities. By compressing such LLMs via quantization to 3-4 bits per parameter, they can fit into memory-limited devices such as laptops and mobile phones, enabling personalized use. However, quantization down to 3-4 ... | SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression |
d212657478 | We hypothesize that curiosity is a mechanism found by evolution that encourages meaningful exploration early in an agent's life in order to expose it to experiences that enable it to obtain high rewards over the course of its lifetime. We formulate the problem of generating curious behavior as one of meta-learning: an ... | META-LEARNING CURIOSITY ALGORITHMS |
d227161986 | We propose PermaKey, a novel approach to representation learning based on object keypoints. It leverages the predictability of local image regions from spatial neighborhoods to identify salient regions that correspond to object parts, which are then converted to keypoints. Unlike prior approaches, it utilizes predictab... | UNSUPERVISED OBJECT KEYPOINT LEARNING USING LOCAL SPATIAL PREDICTABILITY |
d259095948 | Large language models are powerful systems that excel at many tasks, ranging from translation to mathematical reasoning. Yet, at the same time, these models often show unhuman-like characteristics. In the present paper, we address this gap and ask whether large language models can be turned into cognitive models. We fi... | Turning large language models into cognitive models |
d208006294 | The impressive performance of neural networks on natural language processing tasks attributes to their ability to model complicated word and phrase interactions.Existing flat, word level explanations of predictions hardly unveil how neural networks handle compositional semantics to reach predictions.To tackle the chall... | Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models |
d251252927 | Graph Convolutional Networks (GCNs) are one of the most popular architectures that are used to solve classification problems accompanied by graphical information. We present a rigorous theoretical understanding of the effects of graph convolutions in multi-layer networks. We study these effects through the node classif... | Effects of Graph Convolutions in Multi-layer Networks |
d3475375 | We consider the problem of training generative models with a Generative Adversarial Network (GAN). Although GANs can accurately model complex distributions, they are known to be difficult to train due to instabilities caused by a difficult minimax optimization problem. In this paper, we view the problem of training GAN... | An Online Learning Approach to Generative Adversarial Networks |
d108306455 | We introduce an approach to model surface properties governing bounces in everyday scenes. Our model learns end-to-end, starting from sensor inputs, to predict post-bounce trajectories and infer two underlying physical properties that govern bouncing -restitution and effective collision normals. Our model, Bounce and L... | BOUNCE AND LEARN: MODELING SCENE DYNAMICS WITH REAL-WORLD BOUNCES |
d256194054 | Figure 1: Extrapolating from one image. Strongly augmented patches from a single image are used to train a student (S) to distinguish semantic classes, such as those in ImageNet. The student neural network is initialized randomly and learns from a pretrained teacher (T) via KL-divergence. Although almost none of target... | THE AUGMENTED IMAGE PRIOR: DISTILLING 1000 CLASSES BY EXTRAPOLATING FROM A SINGLE IMAGE |
d249431433 | The recent success of Vision Transformers is shaking the long dominance of Convolutional Neural Networks (CNNs) in image recognition for a decade. Specifically, in terms of robustness on out-of-distribution samples, recent research finds that Transformers are inherently more robust than CNNs, regardless of different t... | CAN CNNS BE MORE ROBUST THAN TRANSFORMERS? |
d261823404 | Creating diverse and high-quality 3D assets with an automatic generative model is highly desirable.Despite extensive efforts on 3D generation, most existing works focus on the generation of a single category or a few categories.In this paper, we introduce a diffusion-based feed-forward framework for synthesizing massiv... | LARGE-VOCABULARY 3D DIFFUSION MODEL WITH TRANSFORMER |
d257495957 | The existing model compression methods via structured pruning typically require complicated multi-stage procedures. Each individual stage necessitates numerous engineering efforts and domain-knowledge from the end-users which prevent their wider applications onto broader scenarios. We propose the second generation of O... | OTOV2: AUTOMATIC, GENERIC, USER-FRIENDLY |
d68111200 | Neural network quantization is becoming an industry standard to efficiently deploy deep learning models on hardware platforms, such as CPU, GPU, TPU, and FPGAs. However, we observe that the conventional quantization approaches are vulnerable to adversarial attacks. This paper aims to raise people's awareness about the ... | DEFENSIVE QUANTIZATION: WHEN EFFICIENCY MEETS ROBUSTNESS |
d258887825 | Minimax problems are notoriously challenging to optimize. However, we demonstrate that the two-timescale extragradient can be a viable solution. By utilizing dynamical systems theory, we show that it converges to points that satisfy the second-order necessary condition of local minimax points, under a mild condition. T... | Two-timescale Extragradient for Finding Local Minimax Points |
d263829263 | We contribute to the study of formal languages that can be recognized by transformer encoders.We focus on two self-attention mechanisms: (1) UHAT (Unique Hard Attention Transformers) and (2) AHAT (Average Hard Attention Transformers).UHAT encoders are known to recognize only languages inside the circuit complexity clas... | Logical Languages Accepted by Transformer Encoders with Hard Attention |
d3568073 | We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes i... | PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION |
d204401860 | We consider off-policy policy evaluation when the trajectory data are generated by multiple behavior policies. Recent work has shown the key role played by the state or state-action stationary distribution corrections in the infinite horizon context for off-policy policy evaluation. We propose estimated mixture policy ... | INFINITE-HORIZON OFF-POLICY POLICY EVALUATION WITH MULTIPLE BEHAVIOR POLICIES |
d212414722 | Scalability in terms of object density in a scene is a primary challenge in unsupervised sequential object-oriented representation learning.Most of the previous models have been shown to work only on scenes with a few objects.In this paper, we propose SCALOR, a probabilistic generative world model for learning SCALable... | SCALOR: GENERATIVE WORLD MODELS WITH SCALABLE OBJECT REPRESENTATIONS |
d262054258 | It has long been established that predictive models can be transformed into lossless compressors and vice versa.Incidentally, in recent years, the machine learning community has focused on training increasingly large and powerful self-supervised (language) models.Since these large language models exhibit impressive pre... | Language Modeling Is Compression |
d209439843 | State-of-the-art machine learning methods exhibit limited compositional generalization. At the same time, there is a lack of realistic benchmarks that comprehensively measure this ability, which makes it challenging to find and evaluate improvements. We introduce a novel method to systematically construct such benchmar... | MEASURING COMPOSITIONAL GENERALIZATION: A COMPREHENSIVE METHOD ON REALISTIC DATA |
d53831933 | We focus on the problem of learning a single motor module that can flexibly express a range of behaviors for the control of high-dimensional physically simulated humanoids. To do this, we propose a motor architecture that has the general structure of an inverse model with a latent-variable bottleneck. We show that it i... | NEURAL PROBABILISTIC MOTOR PRIMITIVES FOR HUMANOID CONTROL |
d246442021 | Learning causal relationships in high-dimensional data (images, videos) is a hard task, as they are often defined on low-dimensional manifolds and must be extracted from complex signals dominated by appearance, lighting, textures and also spurious correlations in the data.We present a method for learning counterfactual... | FILTERED-COPHY: UNSUPERVISED LEARNING OF COUNTERFACTUAL PHYSICS IN PIXEL SPACE |
d4862861 | Contrary to most natural language processing research, which makes use of static datasets, humans learn language interactively, grounded in an environment. In this work we propose an interactive learning procedure called Mechanical Turker Descent (MTD) and use it to train agents to execute natural language commands gro... | MASTERING THE DUNGEON: GROUNDED LANGUAGE LEARNING BY MECHANICAL TURKER DESCENT |
d247594872 | Graph Convolutional Networks (GCNs) is the state-of-the-art method for learning graph-structured data, and training large-scale GCNs requires distributed training across multiple accelerators such that each accelerator is able to hold a partitioned subgraph. However, distributed GCN training incurs prohibitive overhead... | PIPEGCN: EFFICIENT FULL-GRAPH TRAINING OF GRAPH CONVOLUTIONAL NETWORKS WITH PIPELINED FEATURE COMMUNICATION |
d252873057 | While instance-level explanation of GNN is a well-studied problem with plenty of approaches being developed, providing a global explanation for the behaviour of a GNN is much less explored, despite its potential in interpretability and debugging. Existing solutions either simply list local explanations for a given clas... | GLOBAL EXPLAINABILITY OF GNNS VIA LOGIC COMBINATION OF LEARNED CONCEPTS |
d260203353 | Normalising Flows are non-parametric statistical models characterised by their dual capabilities of density estimation and generation.This duality requires an inherently invertible architecture.However, the requirement of invertibility imposes constraints on their expressiveness, necessitating a large number of paramet... | Kernelised Normalising Flows |
d238744039 | With little to no parallel data available for programming languages, unsupervised methods are well-suited to source code translation. However, the majority of unsupervised machine translation approaches rely on back-translation, a method developed in the context of natural language translation and one that inherently i... | LEVERAGING AUTOMATED UNIT TESTS FOR UNSUPERVISED CODE TRANSLATION |
d208910151 | We design a new provably efficient algorithm for episodic reinforcement learning with generalized linear function approximation. We analyze the algorithm under a new expressivity assumption that we call "optimistic closure," which is strictly weaker than assumptions from prior analyses for the linear setting. With opti... | Optimism in Reinforcement Learning with Generalized Linear Function Approximation |
d213392702 | Ensuring robustness of Deep Neural Networks (DNNs) is crucial to their adoption in safety-critical applications such as self-driving cars, drones, and healthcare. Notably, DNNs are vulnerable to adversarial attacks in which small input perturbations can produce catastrophic misclassifications. In this work, we propose ... | EMPIR: ENSEMBLES OF MIXED PRECISION DEEP NETWORKS FOR INCREASED ROBUSTNESS AGAINST ADVERSARIAL ATTACKS |
d219401642 | Modern text-to-speech synthesis pipelines typically involve multiple processing stages, each of which is designed or learnt independently from the rest. In this work, we take on the challenging task of learning to synthesise speech from normalised text or phonemes in an end-to-end manner, resulting in models which oper... | End-to-End Adversarial Text-to-Speech |
d259137503 | We design a new family of hybrid CNN-ViT neural networks, named FasterViT, with a focus on high image throughput for computer vision (CV) applications. FasterViT combines the benefits of fast local representation learning in CNNs and global modeling properties in ViT. Our newly introduced Hierarchical Attention (HAT) a... | FasterViT: Fast Vision Transformers with Hierarchical Attention |
d256105681 | The pursuit of long-term fairness involves the interplay between decision-making and the underlying data generating process. In this paper, through causal modeling with a directed acyclic graph (DAG) on the decision-distribution interplay, we investigate the possibility of achieving long-term fairness from a dynamic pe... | TIER BALANCING: TOWARDS DYNAMIC FAIRNESS OVER UNDERLYING CAUSAL FACTORS |
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