_id stringlengths 4 10 | text stringlengths 0 18.4k | title stringlengths 0 8.56k |
|---|---|---|
d261530381 | Federated Learning (FL) has gained significant attraction due to its ability to enable privacy-preserving training over decentralized data.Current literature in FL mostly focuses on single-task learning.However, over time, new tasks may appear in the clients and the global model should learn these tasks without forgetting previous tasks.This real-world scenario is known as Continual Federated Learning (CFL).The main challenge of CFL is Global Catastrophic Forgetting, which corresponds to the fact that when the global model is trained on new tasks, its performance on old tasks decreases.There have been a few recent works on CFL to propose methods that aim to address the global catastrophic forgetting problem.However, these works either have unrealistic assumptions on the availability of past data samples or violate the privacy principles of FL.We propose a novel method, Federated Orthogonal Training (FOT), to overcome these drawbacks and address the global catastrophic forgetting in CFL.Our algorithm extracts the global input subspace of each layer for old tasks and modifies the aggregated updates of new tasks such that they are orthogonal to the global principal subspace of old tasks for each layer.This decreases the interference between tasks, which is the main cause for forgetting.We empirically show that FOT outperforms state-of-the-art continual learning methods in the CFL setting, achieving an average accuracy gain of up to 15% with 27% lower forgetting while only incurring a minimal computation and communication cost. | FEDERATED ORTHOGONAL TRAINING: MITIGATING GLOBAL CATASTROPHIC FORGETTING IN CONTINUAL FEDERATED LEARNING |
d235606307 | Multimodal variational autoencoders (VAEs) seek to model the joint distribution over heterogeneous data (e.g. vision, language), whilst also capturing a shared representation across such modalities. Prior work has typically combined information from the modalities by reconciling idiosyncratic representations directly in the recognition model through explicit products, mixtures, or other such factorisations. Here we introduce a novel alternative, the Mutually supErvised Multimodal VAE (MEME), that avoids such explicit combinations by repurposing semisupervised VAEs to combine information between modalities implicitly through mutual supervision. This formulation naturally allows learning from partiallyobserved data where some modalities can be entirely missing-something that most existing approaches either cannot handle, or do so to a limited extent. We demonstrate that MEME outperforms baselines on standard metrics across both partial and complete observation schemes on the MNIST-SVHN (image-image) and CUB (image-text) datasets 1 . We also contrast the quality of the representations learnt by mutual supervision against standard approaches and observe interesting trends in its ability to capture relatedness between data. | LEARNING MULTIMODAL VAES THROUGH MUTUAL SUPERVISION |
d195886377 | The ability to forecast a set of likely yet diverse possible future behaviors of an agent (e.g., future trajectories of a pedestrian) is essential for safety-critical perception systems (e.g., autonomous vehicles). In particular, a set of possible future behaviors generated by the system must be diverse to account for all possible outcomes in order to take necessary safety precautions. It is not sufficient to maintain a set of the most likely future outcomes because the set may only contain perturbations of a dominating single outcome (major mode). While generative models such as variational autoencoders (VAEs) have been shown to be a powerful tool for learning a distribution over future trajectories, randomly drawn samples from the learned implicit likelihood model may not be diverse -the likelihood model is derived from the training data distribution and the samples will concentrate around the major mode of the data. In this work, we propose to learn a diversity sampling function (DSF) that generates a diverse yet likely set of future trajectories. The DSF maps forecasting context features to a set of latent codes which can be decoded by a generative model (e.g., VAE) into a set of diverse trajectory samples. Concretely, the process of identifying the diverse set of samples is posed as DSF parameter estimation. To learn the parameters of the DSF, the diversity of the trajectory samples is evaluated by a diversity loss based on a determinantal point process (DPP). Gradient descent is performed over the DSF parameters, which in turn moves the latent codes of the sample set to find an optimal set of diverse yet likely trajectories. Our method is a novel application of DPPs to optimize a set of items (forecasted trajectories) in continuous space. We demonstrate the diversity of the trajectories produced by our approach on both low-dimensional 2D trajectory data and high-dimensional human motion data. (Video 1 ) | DIVERSE TRAJECTORY FORECASTING WITH DETERMINANTAL POINT PROCESSES |
d214002473 | We propose a model that is able to perform unsupervised physical parameter estimation of systems from video, where the differential equations governing the scene dynamics are known, but labeled states or objects are not available. Existing physical scene understanding methods require either object state supervision, or do not integrate with differentiable physics to learn interpretable system parameters and states. We address this problem through a physics-as-inverse-graphics approach that brings together vision-as-inverse-graphics and differentiable physics engines, enabling objects and explicit state and velocity representations to be discovered. This framework allows us to perform long term extrapolative video prediction, as well as vision-based model-predictive control. Our approach significantly outperforms related unsupervised methods in long-term future frame prediction of systems with interacting objects (such as ball-spring or 3-body gravitational systems), due to its ability to build dynamics into the model as an inductive bias. We further show the value of this tight vision-physics integration by demonstrating data-efficient learning of vision-actuated model-based control for a pendulum system. We also show that the controller's interpretability provides unique capabilities in goal-driven control and physical reasoning for zero-data adaptation. | PHYSICS-AS-INVERSE-GRAPHICS: UNSUPERVISED PHYSICAL PARAMETER ESTIMATION FROM VIDEO |
d254017742 | Particle-based variational inference (VI) minimizes the KL divergence between model samples and the target posterior with gradient flow estimates. With the popularity of Stein variational gradient descent (SVGD), the focus of particlebased VI algorithms has been on the properties of functions in Reproducing Kernel Hilbert Space (RKHS) to approximate the gradient flow. However, the requirement of RKHS restricts the function class and algorithmic flexibility. This paper offers a general solution to this problem by introducing a functional regularization term that encompasses the RKHS norm as a special case. This allows us to propose a new particle-based VI algorithm called preconditioned functional gradient flow (PFG). Compared to SVGD, PFG has several advantages. It has a larger function class, improved scalability in large particle-size scenarios, better adaptation to ill-conditioned distributions, and provable continuous-time convergence in KL divergence. Additionally, non-linear function classes such as neural networks can be incorporated to estimate the gradient flow. Our theory and experiments demonstrate the effectiveness of the proposed framework. | PARTICLE-BASED VARIATIONAL INFERENCE WITH PRECONDITIONED FUNCTIONAL GRADIENT FLOW |
d220793552 | While successful for various computer vision tasks, deep neural networks have shown to be vulnerable to texture style shifts and small perturbations to which humans are robust. Hence, our goal is to train models in such a way that improves their robustness to these perturbations. We are motivated by the approximately shape-preserving property of randomized convolutions, which is due to distance preservation under random linear transforms. Intuitively, randomized convolutions create an infinite number of new domains with similar object shapes but random local texture. Therefore, we explore using outputs of multi-scale random convolutions as new images or mixing them with the original images during training. When applying a network trained with our approach to unseen domains, our method consistently improves the performance on domain generalization benchmarks and is scalable to ImageNet. Especially for the challenging scenario of generalizing to the sketch domain in PACS and to ImageNet-Sketch, our method outperforms state-of-art methods by a large margin. More interestingly, our method can benefit downstream tasks by providing a more robust pretrained visual representation. 1 | Robust and Generalizable Visual Representation Learning via Random Convolutions |
d256105351 | Complex Query Answering (CQA) over Knowledge Graphs (KGs) has attracted a lot of attention to potentially support many applications. Given that KGs are usually incomplete, neural models are proposed to answer the logical queries by parameterizing set operators with complex neural networks. However, such methods usually train neural set operators with a large number of entity and relation embeddings from the zero, where whether and how the embeddings or the neural set operators contribute to the performance remains not clear. In this paper, we propose a simple framework for complex query answering that decomposes the KG embeddings from neural set operators. We propose to represent the complex queries into the query graph. On top of the query graph, we propose the Logical Message Passing Neural Network (LMPNN) that connects the local one-hop inferences on atomic formulas to the global logical reasoning for complex query answering. We leverage existing effective KG embeddings to conduct one-hop inferences on atomic formulas, the results of which are regarded as the messages passed in LMPNN. The reasoning process over the overall logical formulas is turned into the forward pass of LMPNN that incrementally aggregates local information to finally predict the answers' embeddings. The complex logical inference across different types of queries will then be learned from training examples based on the LMPNN architecture. Theoretically, our query-graph representation is more general than the prevailing operator-tree formulation, so our approach applies to a broader range of complex KG queries. Empirically, our approach yields a new state-of-the-art neural CQA model. Our research bridges the gap between complex KG query answering tasks and the long-standing achievements of knowledge graph representation learning. Our implementation can be found at https://github.com/HKUST-KnowComp/LMPNN.Recently, neural models for Complex Query Answering (CQA) have been proposed to complete the unobserved knowledge graph and answer the complex query simultaneously. These models aim to address complex queries that belong to an important subset of the first-order queries. Formally speaking, the complex queries are Existentially quantified First Order queries and has a single free 1 arXiv:2301.08859v4 [cs.LG] | LOGICAL MESSAGE PASSING NETWORKS WITH ONE-HOP INFERENCE ON ATOMIC FORMULAS |
d4630420 | Methods that learn representations of nodes in a graph play a critical role in network analysis since they enable many downstream learning tasks. We propose Graph2Gauss -an approach that can efficiently learn versatile node embeddings on large scale (attributed) graphs that show strong performance on tasks such as link prediction and node classification. Unlike most approaches that represent nodes as point vectors in a low-dimensional continuous space, we embed each node as a Gaussian distribution, allowing us to capture uncertainty about the representation. Furthermore, we propose an unsupervised method that handles inductive learning scenarios and is applicable to different types of graphs: plain/attributed, directed/undirected. By leveraging both the network structure and the associated node attributes, we are able to generalize to unseen nodes without additional training. To learn the embeddings we adopt a personalized ranking formulation w.r.t. the node distances that exploits the natural ordering of the nodes imposed by the network structure. Experiments on real world networks demonstrate the high performance of our approach, outperforming state-of-the-art network embedding methods on several different tasks. Additionally, we demonstrate the benefits of modeling uncertainty -by analyzing it we can estimate neighborhood diversity and detect the intrinsic latent dimensionality of a graph. | DEEP GAUSSIAN EMBEDDING OF GRAPHS: UNSUPERVISED INDUCTIVE LEARNING VIA RANKING |
d259075184 | A promising direction for pre-training 3D point clouds is to leverage the massive amount of data in 2D, whereas the domain gap between 2D and 3D creates a fundamental challenge.This paper proposes a novel approach to point-cloud pre-training that learns 3D representations by leveraging pre-trained 2D networks.Different from the popular practice of predicting 2D features first and then obtaining 3D features through dimensionality lifting, our approach directly uses a 3D network for feature extraction.We train the 3D feature extraction network with the help of the novel 2D knowledge transfer loss, which enforces the 2D projections of the 3D feature to be consistent with the output of pre-trained 2D networks.To prevent the feature from discarding 3D signals, we introduce the multi-view consistency loss that additionally encourages the projected 2D feature representations to capture pixel-wise correspondences across different views.Such correspondences induce 3D geometry and effectively retain 3D features in the projected 2D features.Experimental results demonstrate that our pre-trained model can be successfully transferred to various downstream tasks, including 3D shape classification, part segmentation, 3D object detection, and semantic segmentation, achieving state-of-the-art performance. | MULTI-VIEW REPRESENTATION IS WHAT YOU NEED FOR POINT-CLOUD PRE-TRAINING |
d263153393 | We propose to replace vector quantization (VQ) in the latent representation of VQ-VAEs with a simple scheme termed finite scalar quantization (FSQ), where we project the VAE representation down to a few dimensions (typically less than 10).Each dimension is quantized to a small set of fixed values, leading to an (implicit) codebook given by the product of these sets.By appropriately choosing the number of dimensions and values each dimension can take, we obtain the same codebook size as in VQ.On top of such discrete representations, we can train the same models that have been trained on VQ-VAE representations.For example, autoregressive and masked transformer models for image generation, multimodal generation, and dense prediction computer vision tasks.Concretely, we employ FSQ with MaskGIT for image generation, and with UViM for depth estimation, colorization, and panoptic segmentation.Despite the much simpler design of FSQ, we obtain competitive performance in all these tasks.We emphasize that FSQ does not suffer from codebook collapse and does not need the complex machinery employed in VQ (commitment losses, codebook reseeding, code splitting, entropy penalties, etc.) to learn expressive discrete representations.Code on GitHub. | FINITE SCALAR QUANTIZATION: VQ-VAE MADE SIMPLE |
d258048805 | Generative modelling over continuous-time geometric constructs, a.k.a chirographic data such as handwriting, sketches, drawings etc., have been accomplished through autoregressive distributions. Such strictly-ordered discrete factorization however falls short of capturing key properties of chirographic data -it fails to build holistic understanding of the temporal concept due to one-way visibility (causality). Consequently, temporal data has been modelled as discrete token sequences of fixed sampling rate instead of capturing the true underlying concept. In this paper, we introduce a powerful model-class namely Denoising Diffusion Probabilistic Models or DDPMs for chirographic data that specifically addresses these flaws. Our model named "CHIRODIFF", being non-autoregressive, learns to capture holistic concepts and therefore remains resilient to higher temporal sampling rate up to a good extent. Moreover, we show that many important downstream utilities (e.g. conditional sampling, creative mixing) can be flexibly implemented using CHIRODIFF. We further show some unique use-cases like stochastic vectorization, de-noising/healing, abstraction are also possible with this model-class. We perform quantitative and qualitative evaluation of our framework on relevant datasets and found it to be better or on par with competing approaches. Please visit our project page for more details: https://ayandas.me/chirodiff. | CHIRODIFF: MODELLING CHIROGRAPHIC DATA WITH DIFFUSION MODELS |
d221971169 | Although neural language models are effective at capturing statistics of natural language, their representations are challenging to interpret. In particular, it is unclear how these models retain information over multiple timescales. In this work, we construct explicitly multi-timescale language models by manipulating the input and forget gate biases in a long short-term memory (LSTM) network. The distribution of timescales is selected to approximate power law statistics of natural language through a combination of exponentially decaying memory cells. We then empirically analyze the timescale of information routed through each part of the model using word ablation experiments and forget gate visualizations. These experiments show that the multi-timescale model successfully learns representations at the desired timescales, and that the distribution includes longer timescales than a standard LSTM. Further, information about high-, mid-, and low-frequency words is routed preferentially through units with the appropriate timescales. Thus we show how to construct language models with interpretable representations of different information timescales. | Multi-timescale representation learning in LSTM Language Models |
d259203153 | Federated learning (FL) is a distributed machine learning framework where the global model of a central server is trained via multiple collaborative steps by participating clients without sharing their data.While being a flexible framework, where the distribution of local data, participation rate, and computing power of each client can greatly vary, such flexibility gives rise to many new challenges, especially in the hyperparameter tuning on the client side.We propose ∆-SGD, a simple step size rule for SGD that enables each client to use its own step size by adapting to the local smoothness of the function each client is optimizing.We provide theoretical and empirical results where the benefit of the client adaptivity is shown in various FL scenarios. 1 | ADAPTIVE FEDERATED LEARNING WITH AUTO-TUNED CLIENTS |
d252715881 | The idea of embedding optimization problems into deep neural networks as optimization layers to encode constraints and inductive priors has taken hold in recent years. Most existing methods focus on implicitly differentiating Karush-Kuhn-Tucker (KKT) conditions in a way that requires expensive computations on the Jacobian matrix, which can be slow and memory-intensive. In this paper, we developed a new framework, named Alternating Differentiation (Alt-Diff), that differentiates optimization problems (here, specifically in the form of convex optimization problems with polyhedral constraints) in a fast and recursive way. Alt-Diff decouples the differentiation procedure into a primal update and a dual update in an alternating way. Accordingly, Alt-Diff substantially decreases the dimensions of the Jacobian matrix especially for optimization with largescale constraints and thus increases the computational speed of implicit differentiation. We show that the gradients obtained by Alt-Diff are consistent with those obtained by differentiating KKT conditions. In addition, we propose to truncate Alt-Diff to further accelerate the computational speed. Under some standard assumptions, we show that the truncation error of gradients is upper bounded by the same order of variables' estimation error. Therefore, Alt-Diff can be truncated to further increase computational speed without sacrificing much accuracy. A series of comprehensive experiments validate the superiority of Alt-Diff. | Alternating Differentiation for Optimization Layers |
d52894384 | Humans learn to solve tasks of increasing complexity by building on top of previously acquired knowledge. Typically, there exists a natural progression in the tasks that we learn -most do not require completely independent solutions, but can be broken down into simpler subtasks. We propose to represent a solver for each task as a neural module that calls existing modules (solvers for simpler tasks) in a functional program-like manner. Lower modules are a black box to the calling module, and communicate only via a query and an output. Thus, a module for a new task learns to query existing modules and composes their outputs in order to produce its own output. Our model effectively combines previous skill-sets, does not suffer from forgetting, and is fully differentiable. We test our model in learning a set of visual reasoning tasks, and demonstrate improved performances in all tasks by learning progressively. By evaluating the reasoning process using human judges, we show that our model is more interpretable than an attention-based baseline. | VISUAL REASONING BY PROGRESSIVE MODULE NETWORKS |
d249017891 | Animals thrive in a constantly changing environment and leverage the temporal structure to learn well-factorized causal representations. In contrast, traditional neural networks suffer from forgetting in changing environments and many methods have been proposed to limit forgetting with different trade-offs. Inspired by the brain thalamocortical circuit, we introduce a simple algorithm that uses optimization at inference time to generate internal representations of the current task dynamically. The algorithm alternates between updating the model weights and a latent task embedding, allowing the agent to parse the stream of temporal experience into discrete events and organize learning about them. On a continual learning benchmark, it achieves competitive end average accuracy by mitigating forgetting, but importantly, by requiring the model to adapt through latent updates, it organizes knowledge into flexible structures with a cognitive interface to control them. Tasks later in the sequence can be solved through knowledge transfer as they become reachable within the well-factorized latent space. The algorithm meets many of the desiderata of an ideal continually learning agent in open-ended environments, and its simplicity suggests fundamental computations in circuits with abundant feedback control loops such as the thalamocortical circuits in the brain | THALAMUS: A BRAIN-INSPIRED ALGORITHM FOR BIOLOGICALLY-PLAUSIBLE CONTINUAL LEARNING AND DISENTANGLED REPRESENTATIONS |
d57759319 | Learning a policy using only observational data is challenging because the distribution of states it induces at execution time may differ from the distribution observed during training. We propose to train a policy by unrolling a learned model of the environment dynamics over multiple time steps while explicitly penalizing two costs: the original cost the policy seeks to optimize, and an uncertainty cost which represents its divergence from the states it is trained on. We measure this second cost by using the uncertainty of the dynamics model about its own predictions, using recent ideas from uncertainty estimation for deep networks. We evaluate our approach using a large-scale observational dataset of driving behavior recorded from traffic cameras, and show that we are able to learn effective driving policies from purely observational data, with no environment interaction. * Equal contribution. MH is now at Microsoft Research. | MODEL-PREDICTIVE POLICY LEARNING WITH UNCERTAINTY REGULARIZATION FOR DRIVING IN DENSE TRAFFIC |
d257631995 | Recently, graph-based planning algorithms have gained much attention to solve goal-conditioned reinforcement learning (RL) tasks: they provide a sequence of subgoals to reach the target-goal, and the agents learn to execute subgoalconditioned policies. However, the sample-efficiency of such RL schemes still remains a challenge, particularly for long-horizon tasks. To address this issue, we present a simple yet effective self-imitation scheme which distills a subgoalconditioned policy into the target-goal-conditioned policy. Our intuition here is that to reach a target-goal, an agent should pass through a subgoal, so target-goal-and subgoal-conditioned policies should be similar to each other. We also propose a novel scheme of stochastically skipping executed subgoals in a planned path, which further improves performance. Unlike prior methods that only utilize graph-based planning in an execution phase, our method transfers knowledge from a planner along with a graph into policy learning. We empirically show that our method can significantly boost the sample-efficiency of the existing goal-conditioned RL methods under various long-horizon control tasks. 1 | IMITATING GRAPH-BASED PLANNING WITH GOAL-CONDITIONED POLICIES |
d231839495 | While deep neural networks show great performance on fitting to the training distribution, improving the networks' generalization performance to the test distribution and robustness to the sensitivity to input perturbations still remain as a challenge. Although a number of mixup based augmentation strategies have been proposed to partially address them, it remains unclear as to how to best utilize the supervisory signal within each input data for mixup from the optimization perspective. We propose a new perspective on batch mixup and formulate the optimal construction of a batch of mixup data maximizing the data saliency measure of each individual mixup data and encouraging the supermodular diversity among the constructed mixup data. This leads to a novel discrete optimization problem minimizing the difference between submodular functions. We also propose an efficient modular approximation based iterative submodular minimization algorithm for efficient mixup computation per each minibatch suitable for minibatch based neural network training. Our experiments show the proposed method achieves the state of the art generalization, calibration, and weakly supervised localization results compared to other mixup methods. The source code is available at https://github.com/snu-mllab/Co-Mixup. Weasel (0.8) Deer (0.2) Bird (0.4) Fish (0.6) Bird (0.4) Dog (0.6) Shark (1.0) Weasel (0.6) Deer (0.4) Weasel (0.5) Deer (0.5) Random sampled pair Bird (0.2) Dog (0.3) Fish (0.5) | Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity |
d257353458 | Real world applications of Reinforcement Learning (RL) are often partially observable, thus requiring memory. Despite this, partial observability is still largely ignored by contemporary RL benchmarks and libraries. We introduce Partially Observable Process Gym (POPGym), a two-part library containing (1) a diverse collection of 15 partially observable environments, each with multiple difficulties and(2)implementations of 13 memory model baselines -the most in a single RL library. Existing partially observable benchmarks tend to fixate on 3D visual navigation, which is computationally expensive and only one type of POMDP. In contrast, POPGym environments are diverse, produce smaller observations, use less memory, and often converge within two hours of training on a consumer-grade GPU. We implement our high-level memory API and memory baselines on top of the popular RLlib framework, providing plug-andplay compatibility with various training algorithms, exploration strategies, and distributed training paradigms. Using POPGym, we execute the largest comparison across RL memory models to date. POPGym is available at https: //github.com/proroklab/popgym.Published as a conference paper at ICLR 2023 1. A comprehensive collection of diverse POMDP tasks.2. The largest collection of memory baseline implementations in an RL library.3. A large-scale, principled comparison across memory models. | POPGYM: BENCHMARKING PARTIALLY OBSERVABLE REINFORCEMENT LEARNING |
d67856680 | Answerer in Questioner's Mind (AQM) is an information-theoretic framework that has been recently proposed for task-oriented dialog systems. AQM benefits from asking a question that would maximize the information gain when it is asked. However, due to its intrinsic nature of explicitly calculating the information gain, AQM has a limitation when the solution space is very large. To address this, we propose AQM+ that can deal with a large-scale problem and ask a question that is more coherent to the current context of the dialog. We evaluate our method on GuessWhich, a challenging task-oriented visual dialog problem, where the number of candidate classes is near 10K. Our experimental results and ablation studies show that AQM+ outperforms the state-of-the-art models by a remarkable margin with a reasonable approximation. In particular, the proposed AQM+ reduces more than 60% of error as the dialog proceeds, while the comparative algorithms diminish the error by less than 6%. Based on our results, we argue that AQM+ is a general task-oriented dialog algorithm that can be applied for non-yes-or-no responses. | LARGE-SCALE ANSWERER IN QUESTIONER'S MIND FOR VISUAL DIALOG QUESTION GENERATION |
d231740484 | Deep learning models require a large amount of data to perform well. When data is scarce for a target task, we can transfer the knowledge gained by training on similar tasks to quickly learn the target. A successful approach is meta-learning, or learning to learn a distribution of tasks, where learning is represented by an outer loop, and to learn by an inner loop of gradient descent. However, a number of recent empirical studies argue that the inner loop is unnecessary and more simple models work equally well or even better. We study the performance of MAML as a function of the learning rate of the inner loop, where zero learning rate implies that there is no inner loop. Using random matrix theory and exact solutions of linear models, we calculate an algebraic expression for the test loss of MAML applied to mixed linear regression and nonlinear regression with overparameterized models. Surprisingly, while the optimal learning rate for adaptation is positive, we find that the optimal learning rate for training is always negative, a setting that has never been considered before. Therefore, not only does the performance increase by decreasing the learning rate to zero, as suggested by recent work, but it can be increased even further by decreasing the learning rate to negative values. These results help clarify under what circumstances meta-learning performs best.arXiv:2102.00940v1 [cs.LG] 1 Feb 2021Published as a conference paper at ICLR 2021 function of the inner loop learning rate during meta-training. Setting this learning rate to zero is equivalent to removing the inner loop, as advocated by recent work(Chen et al. (2020a), Tian et al. (2020), Dhillon et al. (2020), Chen et al. (2020b, Raghu et al. (2020)). Surprisingly, we find that the optimal learning rate is negative, thus performance can be increased by reducing the learning rate below zero. In particular, we find the following:• In the problem of mixed linear regression, we prove that the optimal learning rate is always negative in overparameterized models. The same result holds in underparameterized models provided that the optimal learning rate is small in absolute value. We validate the theory by running extensive experiments. • We extend these results to the case of nonlinear regression and wide neural networks, in which the output of can be approximated by a linear function of the parameters (Jacot et al.(2018), Lee et al.(2019)). While in this case we cannot prove that the optimal learning rate is always negative, preliminary experiments suggest that the result holds in this case as well. | META-LEARNING WITH NEGATIVE LEARNING RATES |
d203836948 | We propose an inductive matrix completion model without using side information. By factorizing the (rating) matrix into the product of low-dimensional latent embeddings of rows (users) and columns (items), a majority of existing matrix completion methods are transductive, since the learned embeddings cannot generalize to unseen rows/columns or to new matrices. To make matrix completion inductive, most previous works use content (side information), such as user's age or movie's genre, to make predictions. However, high-quality content is not always available, and can be hard to extract. Under the extreme setting where not any side information is available other than the matrix to complete, can we still learn an inductive matrix completion model? In this paper, we propose an Inductive Graph-based Matrix Completion (IGMC) model to address this problem. IGMC trains a graph neural network (GNN) based purely on 1-hop subgraphs around (user, item) pairs generated from the rating matrix and maps these subgraphs to their corresponding ratings. It achieves highly competitive performance with state-of-the-art transductive baselines. In addition, IGMC is inductive -it can generalize to users/items unseen during the training (given that their interactions exist), and can even transfer to new tasks. Our transfer learning experiments show that a model trained out of the MovieLens dataset can be directly used to predict Douban movie ratings with surprisingly good performance. Our work demonstrates that: 1) it is possible to train inductive matrix completion models without using side information while achieving similar or better performances than state-of-the-art transductive methods; 2) local graph patterns around a (user, item) pair are effective predictors of the rating this user gives to the item; and 3) Long-range dependencies might not be necessary for modeling recommender systems. | INDUCTIVE MATRIX COMPLETION BASED ON GRAPH NEURAL NETWORKS |
d222380524 | We propose a general framework for searching surrogate losses for mainstream semantic segmentation metrics. This is in contrast to existing loss functions manually designed for individual metrics. The searched surrogate losses can generalize well to other datasets and networks. Extensive experiments on PASCAL VOC and Cityscapes demonstrate the effectiveness of our approach. Code shall be released. | AUTO SEG-LOSS: SEARCHING METRIC SURROGATES FOR SEMANTIC SEGMENTATION |
d238744427 | In this paper, we propose THOMAS, a joint multi-agent trajectory prediction framework allowing for an efficient and consistent prediction of multi-agent multimodal trajectories. We present a unified model architecture for simultaneous agent future heatmap estimation, in which we leverage hierarchical and sparse image generation for fast and memory-efficient inference. We propose a learnable trajectory recombination model that takes as input a set of predicted trajectories for each agent and outputs its consistent reordered recombination. This recombination module is able to realign the initially independent modalities so that they do no collide and are coherent with each other. We report our results on the Interaction multi-agent prediction challenge and rank 1 st on the online test leaderboard. | THOMAS: TRAJECTORY HEATMAP OUTPUT WITH LEARNED MULTI-AGENT SAMPLING |
d238419701 | We present a novel framework, InfinityGAN, for arbitrary-sized image generation. The task is associated with several key challenges. First, scaling existing models to an arbitrarily large image size is resource-constrained, in terms of both computation and availability of large-field-of-view training data. InfinityGAN trains and infers in a seamless patch-by-patch manner with low computational resources. Second, large images should be locally and globally consistent, avoid repetitive patterns, and look realistic. To address these, InfinityGAN disentangles global appearances, local structures, and textures. With this formulation, we can generate images with spatial size and level of details not attainable before. Experimental evaluation validates that InfinityGAN generates images with superior realism compared to baselines and features parallelizable inference. Finally, we show several applications unlocked by our approach, such as spatial style fusion, multimodal outpainting, and image inbetweening. All applications can be operated with arbitrary input and output sizes. Style A Style A Style B Style B Training Patch Size (101x101) | INFINITYGAN: TOWARDS INFINITE-PIXEL IMAGE SYNTHESIS |
d209202457 | When performing imitation learning from expert demonstrations, distribution matching is a popular approach, in which one alternates between estimating distribution ratios and then using these ratios as rewards in a standard reinforcement learning (RL) algorithm. Traditionally, estimation of the distribution ratio requires on-policy data, which has caused previous work to either be exorbitantly datainefficient or alter the original objective in a manner that can drastically change its optimum. In this work, we show how the original distribution ratio estimation objective may be transformed in a principled manner to yield a completely off-policy objective. In addition to the data-efficiency that this provides, we are able to show that this objective also renders the use of a separate RL optimization unnecessary. Rather, an imitation policy may be learned directly from this objective without the use of explicit rewards. We call the resulting algorithm ValueDICE and evaluate it on a suite of popular imitation learning benchmarks, finding that it can achieve state-of-the-art sample efficiency and performance. 1 | IMITATION LEARNING VIA OFF-POLICY DISTRIBUTION MATCHING |
d195791369 | Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment. A good model can potentially enable planning algorithms to generate a large variety of behaviors and solve diverse tasks. However, learning an accurate model for complex dynamical systems is difficult, and even then, the model might not generalize well outside the distribution of states on which it was trained. In this work, we combine model-based learning with model-free learning of primitives that make modelbased planning easy. To that end, we aim to answer the question: how can we discover skills whose outcomes are easy to predict? We propose an unsupervised learning algorithm, Dynamics-Aware Discovery of Skills (DADS), which simultaneously discovers predictable behaviors and learns their dynamics. Our method can leverage continuous skill spaces, theoretically, allowing us to learn infinitely many behaviors even for high-dimensional state-spaces. We demonstrate that zero-shot planning in the learned latent space significantly outperforms standard MBRL and model-free goal-conditioned RL, can handle sparsereward tasks, and substantially improves over prior hierarchical RL methods for unsupervised skill discovery. We have open-sourced our implementation at: https://github.com/google-research/dadsFigure 1: A humanoid agent discovers diverse locomotion primitives without any reward using DADS. We show zero-shot generalization to downstream tasks by composing the learned primitives using model predictive control, enabling the agent to follow an online sequence of goals (green markers) without any additional training. * Work done a part of the Google AI Residency program. | DYNAMICS-AWARE UNSUPERVISED DISCOVERY OF SKILLS |
d258999368 | Despite the impressive generalization capabilities of deep neural networks, they have been repeatedly shown to be overconfident when they are wrong. Fixing this issue is known as model calibration, and has consequently received much attention in the form of modified training schemes and post-training calibration procedures such as temperature scaling. While temperature scaling is frequently used because of its simplicity, it is often outperformed by modified training schemes. In this work, we identify a specific bottleneck for the performance of temperature scaling. We show that for empirical risk minimizers for a general set of distributions in which the supports of classes have overlaps, the performance of temperature scaling degrades with the amount of overlap between classes, and asymptotically becomes no better than random when there are a large number of classes. On the other hand, we prove that optimizing a modified form of the empirical risk induced by the Mixup data augmentation technique can in fact lead to reasonably good calibration performance, showing that training-time calibration may be necessary in some situations. We also verify that our theoretical results reflect practice by showing that Mixup significantly outperforms empirical risk minimization (with respect to multiple calibration metrics) on image classification benchmarks with class overlaps introduced in the form of label noise. | ON THE LIMITATIONS OF TEMPERATURE SCALING FOR DISTRIBUTIONS WITH OVERLAPS |
d248239812 | Model-based reinforcement learning (RL) algorithms designed for handling complex visual observations typically learn some sort of latent state representation, either explicitly or implicitly. Standard methods of this sort do not distinguish between functionally relevant aspects of the state and irrelevant distractors, instead aiming to represent all available information equally. We propose a modified objective for model-based RL that, in combination with mutual information maximization, allows us to learn representations and dynamics for visual model-based RL without reconstruction in a way that explicitly prioritizes functionally relevant factors. The key principle behind our design is to integrate a term inspired by variational empowerment into a state-space model based on mutual information. This term prioritizes information that is correlated with action, thus ensuring that functionally relevant factors are captured first. Furthermore, the same empowerment term also promotes faster exploration during the RL process, especially for sparse-reward tasks where the reward signal is insufficient to drive exploration in the early stages of learning. We evaluate the approach on a suite of vision-based robot control tasks with natural video backgrounds, and show that the proposed prioritized information objective outperforms state-of-the-art model based RL approaches with higher sample efficiency and episodic returns. | INFORMATION PRIORITIZATION THROUGH EMPOWERMENT IN VISUAL MODEL-BASED RL |
d219721308 | Determinantal point processes (DPPs) have attracted significant attention from the machine learning community for their ability to model subsets drawn from a large collection of items. Recent work shows that nonsymmetric DPP kernels have significant advantages over symmetric kernels in terms of modeling power and predictive performance. However, the nonsymmetric kernel learning algorithm from prior work has computational complexity that is cubic in the size of the DPP ground set, from which subsets are drawn, making it impractical to use at large scales. In this work, we propose a new decomposition for nonsymmetric DPP kernels that induces linear-time complexity for learning and approximate maximum a posteriori (MAP) inference. We also prove a lower bound on the quality of this MAP approximation. Through evaluation on real-world datasets, we show that our new decomposition not only scales better, but also matches or exceeds the predictive performance of prior work. arXiv:2006.09862v1 [cs.LG] 17 Jun 2020 MAP inference: For the problem of finding the highest-probability subset under a DPP (the "MAP inference" problem), we analyze a standard greedy subset selection algorithm and show that, for low-rank NDPPs, it can be run in time linear in the size of the DPP ground set. We also describe how the same algorithm can be used to do conditioning for next-item prediction tasks. We then prove a lower bound on greedy MAP performance for all NDPPs.We combine the above contributions through experiments that involve learning NDPP kernels and applying MAP inference to these kernels for several real-world datasets. These experiments demonstrate that our scalable decomposition matches or exceeds the predictive performance of prior work. We also show that, for small synthetic kernels generated from learned kernels, the proposed greedy MAP algorithm has an approximation quality that often substantially exceeds the lower bound, with near-optimal performance in many cases.BackgroundConsider a finite set Y = {1, 2, . . . , M } of cardinality M , which we will also denote by [[M ]]. A DPP on [[M ]] defines a probability distribution over all its 2 M subsets. It is parameterized by a matrix L ∈ R M ×M , called the kernel, such that the probability of each subset Y ⊆ [[M ]] is proportional to the determinant of its corresponding principal submatrix: Pr(Y ) ∝ det(L Y ). The normalization constant for this distribution can be expressed as a single M × M determinant: Y ⊆[[M ]] det(L Y ) = det(L + I) [27, Theorem 2.1]. Hence, Pr(Y ) = det(L Y )/ det(L + I).We will use P L to denote this distribution. | Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes |
d204734215 | Many tasks in modern machine learning can be formulated as finding equilibria in sequential games. In particular, two-player zero-sum sequential games, also known as minimax optimization, have received growing interest. It is tempting to apply gradient descent to solve minimax optimization given its popularity and success in supervised learning. However, it has been noted that naive application of gradient descent fails to find some local minimax and can converge to nonlocal-minimax points. In this paper, we propose Follow-the-Ridge (FR), a novel algorithm that provably converges to and only converges to local minimax. We show theoretically that the algorithm addresses the notorious rotational behaviour of gradient dynamics, and is compatible with preconditioning and positive momentum. Empirically, FR solves toy minimax problems and improves the convergence of GAN training compared to the recent minimax optimization algorithms. * These two authors contributed equally. | ON SOLVING MINIMAX OPTIMIZATION LOCALLY: A FOLLOW-THE-RIDGE APPROACH |
d231648420 | Facial recognition systems are increasingly deployed by private corporations, government agencies, and contractors for consumer services and mass surveillance programs alike. These systems are typically built by scraping social media profiles for user images. Adversarial perturbations have been proposed for bypassing facial recognition systems. However, existing methods fail on full-scale systems and commercial APIs. We develop our own adversarial filter that accounts for the entire image processing pipeline and is demonstrably effective against industrial-grade pipelines that include face detection and large scale databases. Additionally, we release an easy-to-use webtool that significantly degrades the accuracy of Amazon Rekognition and the Microsoft Azure Face Recognition API, reducing the accuracy of each to below 1%. * Authors contributed equally. | LOWKEY: LEVERAGING ADVERSARIAL ATTACKS TO PROTECT SOCIAL MEDIA USERS FROM FACIAL RECOGNITION |
d252280608 | Deep learning based approaches like Physics-informed neural networks (PINNs) and DeepONets have shown promise on solving PDE constrained optimization (PDECO) problems. However, existing methods are insufficient to handle those PDE constraints that have a complicated or nonlinear dependency on optimization targets. In this paper, we present a novel bi-level optimization framework to resolve the challenge by decoupling the optimization of the targets and constraints. For the inner loop optimization, we adopt PINNs to solve the PDE constraints only. For the outer loop, we design a novel method by using Broyden's method based on the Implicit Function Theorem (IFT), which is efficient and accurate for approximating hypergradients. We further present theoretical explanations and error analysis of the hypergradients computation. Extensive experiments on multiple large-scale and nonlinear PDE constrained optimization problems demonstrate that our method achieves state-of-the-art results compared with strong baselines. | BI-LEVEL PHYSICS-INFORMED NEURAL NETWORKS FOR PDE CONSTRAINED OPTIMIZATION USING BROYDEN'S HYPERGRADIENTS |
d234790212 | Object goal navigation aims to steer an agent towards a target object based on observations of the agent. It is of pivotal importance to design effective visual representations of the observed scene in determining navigation actions. In this paper, we introduce a Visual Transformer Network (VTNet) for learning informative visual representation in navigation. VTNet is a highly effective structure that embodies two key properties for visual representations: First, the relationships among all the object instances in a scene are exploited; Second, the spatial locations of objects and image regions are emphasized so that directional navigation signals can be learned. Furthermore, we also develop a pre-training scheme to associate the visual representations with navigation signals, and thus facilitate navigation policy learning. In a nutshell, VTNet embeds object and region features with their location cues as spatial-aware descriptors and then incorporates all the encoded descriptors through attention operations to achieve informative representation for navigation. Given such visual representations, agents are able to explore the correlations between visual observations and navigation actions. For example, an agent would prioritize "turning right" over "turning left" when the visual representation emphasizes on the right side of activation map. Experiments in the artificial environment AI2-Thor demonstrate that VTNet significantly outperforms state-of-the-art methods in unseen testing environments. | VTNET: VISUAL TRANSFORMER NETWORK FOR OBJECT GOAL NAVIGATION |
d236965836 | Imitation learning algorithms learn a policy from demonstrations of expert behavior. We show that, for deterministic experts, imitation learning can be done by reduction to reinforcement learning with a stationary reward. Our theoretical analysis both certifies the recovery of expert reward and bounds the total variation distance between the expert and the imitation learner, showing a link to adversarial imitation learning. We conduct experiments which confirm that our reduction works well in practice for continuous control tasks. | IMITATION LEARNING BY REINFORCEMENT LEARNING |
d247518687 | Capturing aleatoric uncertainty is a critical part of many machine learning systems. In deep learning, a common approach to this end is to train a neural network to estimate the parameters of a heteroscedastic Gaussian distribution by maximizing the logarithm of the likelihood function under the observed data. In this work, we examine this approach and identify potential hazards associated with the use of log-likelihood in conjunction with gradient-based optimizers. First, we present a synthetic example illustrating how this approach can lead to very poor but stable parameter estimates. Second, we identify the culprit to be the log-likelihood loss, along with certain conditions that exacerbate the issue. Third, we present an alternative formulation, termed β−NLL, in which each data point's contribution to the loss is weighted by the β-exponentiated variance estimate. We show that using an appropriate β largely mitigates the issue in our illustrative example. Fourth, we evaluate this approach on a range of domains and tasks and show that it achieves considerable improvements and performs more robustly concerning hyperparameters, both in predictive RMSE and log-likelihood criteria. arXiv:2203.09168v2 [cs.LG] 1 Apr 2022 | ON THE PITFALLS OF HETEROSCEDASTIC UNCERTAINTY ESTIMATION WITH PROBABILISTIC NEURAL NETWORKS |
d247594344 | Thanks to the power of representation learning, neural contextual bandit algorithms demonstrate remarkable performance improvement against their classical counterparts. But because their exploration has to be performed in the entire neural network parameter space to obtain nearly optimal regret, the resulting computational cost is prohibitively high. We perturb the rewards when updating the neural network to eliminate the need of explicit exploration and the corresponding computational overhead. We prove that a O( d √ T ) regret upper bound is still achievable under standard regularity conditions, where T is the number of rounds of interactions and d is the effective dimension of a neural tangent kernel matrix. Extensive comparisons with several benchmark contextual bandit algorithms, including two recent neural contextual bandit models, demonstrate the effectiveness and computational efficiency of our proposed neural bandit algorithm. | LEARNING NEURAL CONTEXTUAL BANDITS THROUGH PERTURBED REWARDS |
d257833590 | Disentangling complex data to its latent factors of variation is a fundamental task in representation learning. Existing work on sequential disentanglement mostly provides two factor representations, i.e., it separates the data to time-varying and time-invariant factors. In contrast, we consider multifactor disentanglement in which multiple (more than two) semantic disentangled components are generated. Key to our approach is a strong inductive bias where we assume that the underlying dynamics can be represented linearly in the latent space. Under this assumption, it becomes natural to exploit the recently introduced Koopman autoencoder models. However, disentangled representations are not guaranteed in Koopman approaches, and thus we propose a novel spectral loss term which leads to structured Koopman matrices and disentanglement. Overall, we propose a simple and easy to code new deep model that is fully unsupervised and it supports multifactor disentanglement. We showcase new disentangling abilities such as swapping of individual static factors between characters, and an incremental swap of disentangled factors from the source to the target. Moreover, we evaluate our method extensively on two factor standard benchmark tasks where we significantly improve over competing unsupervised approaches, and we perform competitively in comparison to weaklyand self-supervised state-of-the-art approaches. | MULTIFACTOR SEQUENTIAL DISENTANGLEMENT VIA STRUCTURED KOOPMAN AUTOENCODERS |
d5034059 | For natural language understanding (NLU) technology to be maximally useful, it must be able to process language in a way that is not exclusively tailored to a specific task, genre, or dataset. In pursuit of this objective, we introduce the General Language Understanding Evaluation (GLUE) benchmark, a collection of tools for evaluating and analyzing the performance of models across a diverse set of existing NLU tasks. By including tasks with limited training data, GLUE is designed to favor and encourage models that share general linguistic knowledge across tasks. GLUE also includes a hand-crafted diagnostic test suite that enables detailed linguistic analysis of models. We evaluate baselines based on current methods for transfer and representation learning and find that multi-task training on all our tasks yields better results than training a separate model for each task. However, the low absolute performance of our best model indicates the need for improved general NLU systems. son. 2013. One billion word benchmark for measuring progress in statistical language modeling. arXiv preprint 1312.3005. | GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding |
d220546448 | Although much progress has been made towards robust deep learning, a significant gap in robustness remains between real-world perturbations and more narrowly defined sets typically studied in adversarial defenses. In this paper, we aim to bridge this gap by learning perturbation sets from data, in order to characterize realworld effects for robust training and evaluation. Specifically, we use a conditional generator that defines the perturbation set over a constrained region of the latent space. We formulate desirable properties that measure the quality of a learned perturbation set, and theoretically prove that a conditional variational autoencoder naturally satisfies these criteria. Using this framework, our approach can generate a variety of perturbations at different complexities and scales, ranging from baseline digit transformations, through common image corruptions, to lighting variations. We measure the quality of our learned perturbation sets both quantitatively and qualitatively, finding that our models are capable of producing a diverse set of meaningful perturbations beyond the limited data seen during training. Finally, we leverage our learned perturbation sets to learn models which have improved generalization performance and are empirically and certifiably robust to adversarial image corruptions and adversarial lighting variations. All code and configuration files for reproducing the experiments as well as pretrained model weights can be found at https://github.com/locuslab/perturbation_learning.Preprint. Under review. | Learning perturbation sets for robust machine learning |
d233204603 | We present ReCo, a contrastive learning framework designed at a regional level to assist learning in semantic segmentation. ReCo performs semi-supervised or supervised pixel-level contrastive learning on a sparse set of hard negative pixels, with minimal additional memory footprint. ReCo is easy to implement, being built on top of offthe-shelf segmentation networks, and consistently improves performance in both semisupervised and supervised semantic segmentation methods, achieving smoother segmentation boundaries and faster convergence. The strongest effect is in semi-supervised learning with very few labels. With ReCo, we achieve high quality semantic segmentation models, requiring only 5 examples of each semantic class. Code is available at https://github.com/lorenmt/reco. * Corresponding Author: shikun.liu17@imperial.ac.uk. | Bootstrapping Semantic Segmentation with Regional Contrast |
d208202124 | Approaches to continual learning aim to successfully learn a set of related tasks that arrive in an online manner. Recently, several frameworks have been developed which enable deep learning to be deployed in this learning scenario. A key modelling decision is to what extent the architecture should be shared across tasks. On the one hand, separately modelling each task avoids catastrophic forgetting but it does not support transfer learning and leads to large models.On the other hand, rigidly specifying a shared component and a task-specific part enables task transfer and limits the model size, but it is vulnerable to catastrophic forgetting and restricts the form of task-transfer that can occur.Ideally, the network should adaptively identify which parts of the network to share in a data driven way. Here we introduce such an approach called Continual Learning with Adaptive Weights (CLAW), which is based on probabilistic modelling and variational inference. Experiments show that CLAW achieves state-of-the-art performance on six benchmarks in terms of overall continual learning performance, as measured by classification accuracy, and in terms of addressing catastrophic forgetting. | Continual Learning with Adaptive Weights (CLAW) |
d257365037 | Important research efforts have focused on the design and training of neural networks with a controlled Lipschitz constant.The goal is to increase and sometimes guarantee the robustness against adversarial attacks.Recent promising techniques draw inspirations from different backgrounds to design 1-Lipschitz neural networks, just to name a few: convex potential layers derive from the discretization of continuous dynamical systems, Almost-Orthogonal-Layer proposes a tailored method for matrix rescaling.However, it is today important to consider the recent and promising contributions in the field under a common theoretical lens to better design new and improved layers.This paper introduces a novel algebraic perspective unifying various types of 1-Lipschitz neural networks, including the ones previously mentioned, along with methods based on orthogonality and spectral methods.Interestingly, we show that many existing techniques can be derived and generalized via finding analytical solutions of a common semidefinite programming (SDP) condition.We also prove that AOL biases the scaled weight to the ones which are close to the set of orthogonal matrices in a certain mathematical manner.Moreover, our algebraic condition, combined with the Gershgorin circle theorem, readily leads to new and diverse parameterizations for 1-Lipschitz network layers.Our approach, called SDP-based Lipschitz Layers (SLL), allows us to design non-trivial yet efficient generalization of convex potential layers.Finally, the comprehensive set of experiments on image classification shows that SLLs outperform previous approaches on certified robust accuracy.Code is available at github.com/araujoalexandre/Lipschitz-SLL-Networks.(10/26/2023): Erratum is added in Appendix D. This is an updated version that fixes an implementation issue in the previous version.Due to that implementation issue, the original numerical results in our original ICLR paper are not accurate.We elaborate on the issue and provides some fix in Appendix D. | A UNIFIED ALGEBRAIC PERSPECTIVE ON LIPSCHITZ NEURAL NETWORKS |
d195798643 | Federated learning enables a large amount of edge computing devices to learn a centralized model while keeping all local data on edge devices. As a leading algorithm in this setting, Federated Averaging (FedAvg) runs Stochastic Gradient Descent (SGD) in parallel on a small subset of the total devices and averages the sequences only once in a while. Despite its simplicity, it lacks theoretical guarantees in the federated setting. In this paper, we analyze the convergence of FedAvg on non-iid data. We investigate the effect of different sampling and averaging schemes, which are crucial especially when data are unbalanced. We prove a concise convergence rate of O( 1 T ) for FedAvg with proper sampling and averaging schemes in convex problems, where T is the total number of steps. Our results show that heterogeneity of data slows down the convergence, which is intrinsic in the federated setting. Low device participation rate can be achieved without severely harming the optimization process in federated learning. We show that there is a trade-off between communication efficiency and convergence rate. We analyze the necessity of learning rate decay by taking a linear regression as an example. Our work serves as a guideline for algorithm design in applications of federated learning, where heterogeneity and unbalance of data are the common case. * Equal contribution. | On the Convergence of FedAvg on Non-IID Data |
d238354021 | We introduce Autoregressive Diffusion Models (ARDMs), a model class encompassing and generalizing order-agnostic autoregressive models(Uria et al., 2014)and absorbing discrete diffusion(Austin et al., 2021), which we show are special cases of ARDMs under mild assumptions. ARDMs are simple to implement and easy to train. Unlike standard ARMs, they do not require causal masking of model representations, and can be trained using an efficient objective similar to modern probabilistic diffusion models that scales favourably to highly-dimensional data. At test time, ARDMs support parallel generation which can be adapted to fit any given generation budget. We find that ARDMs require significantly fewer steps than discrete diffusion models to attain the same performance. Finally, we apply ARDMs to lossless compression, and show that they are uniquely suited to this task. Contrary to existing approaches based on bits-back coding, ARDMs obtain compelling results not only on complete datasets, but also on compressing single data points. Moreover, this can be done using a modest number of network calls for (de)compression due to the model's adaptable parallel generation.Published as a conference paper at ICLR 2022 Hoogeboom et al., 2021; Austin et al., 2021) describe a discrete destruction process for which the inverse generative process is learned with categorical distributions. However, the length of these processes may need to be large to attain good performance, which leads to a large number of network calls to sample from or evaluate the likelihood with discrete diffusion.In this work we introduce Autoregressive Diffusion Models (ARDMs), a variant of autoregressive models that learns to generate in any order. ARDMs generalize order agnostic autoregressive models and discrete diffusion models. We show that ARDMs have several benefits: In contrast to standard ARMs, they impose no architectural constraints on the neural networks used to predict the distribution parameters. Further, ARDMs require significantly fewer steps than absorbing models to attain the same performance. In addition, using dynamic programming approaches developed for diffusion models, ARDMs can be parallelized to generate multiple tokens simultaneously without a substantial reduction in performance. Empirically we demonstrate that ARDMs perform similarly to or better than discrete diffusion models while being more efficient in modelling steps. The main contributions of this paper can be summarized as follows: 1) We introduce ARDMs, a variant of order-agnostic ARMs which include the ability to upscale variables. 2) We derive an equivalence between ARDMs and absorbing diffusion under a continuous time limit. 3) We show that ARDMs can have parallelized inference and generation processes, a property that among other things admits competitive lossless compression with a modest number of network calls. This can be seen as a latent variable model and the log-likelihood is derived via Jensen's inequality:Mind that hereafter we leave out σ in our notation to avoid clutter. One approach to train OA-ARMs is the procedure as described byYang et al. (2019)for XLNet. It takes a permutation equivariant network such as a Transformer that is causally masked. Then, inputs are permuted and outputs are permuted back according to a given order, which models the sequence in that specific order. However, such approaches typically suffer in likelihood score and cannot be combined with simple non-equivariant transformations such as convolutional layers.Discrete Diffusion Discrete diffusion models define a destruction process on discrete data. An example is absorbing diffusion (Austin et al., 2021), for which each variable has a probability of decaying to an absorbing state. The opposite process to the destruction process is the learned generative process. This generative process models the distribution over variables that are currently absorbed, and generates these with a probability.AUTOREGRESSIVE DIFFUSION MODELSWe introduce Autoregressive Diffusion Models (ARDMs). ARDMs generate variables in an arbitrary order, one by one. Further, ARDMs are able to upscale variables, such as the bit values of a | AUTOREGRESSIVE DIFFUSION MODELS |
d218720006 | Mirror descent (MD), a well-known first-order method in constrained convex optimization, has recently been shown as an important tool to analyze trust-region algorithms in reinforcement learning (RL). However, there remains a considerable gap between such theoretically analyzed algorithms and the ones used in practice. Inspired by this, we propose an efficient RL algorithm, called mirror descent policy optimization (MDPO). MDPO iteratively updates the policy by approximately solving a trust-region problem, whose objective function consists of two terms: a linearization of the standard RL objective and a proximity term that restricts two consecutive policies to be close to each other. Each update performs this approximation by taking multiple gradient steps on this objective function. We derive on-policy and off-policy variants of MDPO, while emphasizing important design choices motivated by the existing theory of MD in RL. We highlight the connections between on-policy MDPO and two popular trust-region RL algorithms: TRPO and PPO, and show that explicitly enforcing the trust-region constraint is in fact not a necessity for high performance gains in TRPO. We then show how the popular soft actor-critic (SAC) algorithm can be derived by slight modifications of off-policy MDPO. Overall, MDPO is derived from the MD principles, offers a unified approach to viewing a number of popular RL algorithms, and performs better than or on-par with TRPO, PPO, and SAC in a number of continuous control tasks. Code is available at https://github.com/manantomar/ Mirror-Descent-Policy-Optimization. | Mirror Descent Policy Optimization |
d247618912 | Many promising applications of supervised machine learning face hurdles in the acquisition of labeled data in sufficient quantity and quality, creating an expensive bottleneck. To overcome such limitations, techniques that do not depend on ground truth labels have been studied, including weak supervision and generative modeling. While these techniques would seem to be usable in concert, improving one another, how to build an interface between them is not well-understood. In this work, we propose a model fusing programmatic weak supervision and generative adversarial networks and provide theoretical justification motivating this fusion. The proposed approach captures discrete latent variables in the data alongside the weak supervision derived label estimate. Alignment of the two allows for better modeling of sample-dependent accuracies of the weak supervision sources, improving the estimate of unobserved labels. It is the first approach to enable data augmentation through weakly supervised synthetic images and pseudolabels. Additionally, its learned latent variables can be inspected qualitatively. The model outperforms baseline weak supervision label models on a number of multiclass image classification datasets, improves the quality of generated images, and further improves end-model performance through data augmentation with synthetic samples. * | GENERATIVE MODELING HELPS WEAK SUPERVISION (AND VICE VERSA) |
d238857286 | This paper investigates unsupervised learning of Full-Waveform Inversion (FWI), which has been widely used in geophysics to estimate subsurface velocity maps from seismic data. This problem is mathematically formulated by a second order partial differential equation (PDE), but is hard to solve. Moreover, acquiring velocity map is extremely expensive, making it impractical to scale up a supervised approach to train the mapping from seismic data to velocity maps with convolutional neural networks (CNN).We address these difficulties by integrating PDE and CNN in a loop, thus shifting the paradigm to unsupervised learning that only requires seismic data. In particular, we use finite difference to approximate the forward modeling of PDE as a differentiable operator (from velocity map to seismic data) and model its inversion by CNN (from seismic data to velocity map). Hence, we transform the supervised inversion task into an unsupervised seismic data reconstruction task. We also introduce a new large-scale dataset OpenFWI, to establish a more challenging benchmark for the community. Experiment results show that our model (using seismic data alone) yields comparable accuracy to the supervised counterpart (using both seismic data and velocity map). Furthermore, it outperforms the supervised model when involving more seismic data.Figure 1: Schematic illustration of our proposed method, which comprises a CNN to learn an inverse mapping and a differentiable operator to approximate the forward modeling of PDE. * Equal contribution.Dataset is available at https://openfwi-lanl.github.io. | UNSUPERVISED LEARNING OF FULL-WAVEFORM INVERSION: CONNECTING CNN AND PARTIAL DIFFERENTIAL EQUATION IN A LOOP |
d259287063 | Neural networks can be significantly compressed by pruning, yielding sparse models with reduced storage and computational demands while preserving predictive performance.Model soups(Wortsman et al., 2022a)enhance generalization and out-of-distribution (OOD) performance by averaging the parameters of multiple models into a single one, without increasing inference time.However, achieving both sparsity and parameter averaging is challenging as averaging arbitrary sparse models reduces the overall sparsity due to differing sparse connectivities.This work addresses these challenges by demonstrating that exploring a single retraining phase of Iterative Magnitude Pruning (IMP) with varied hyperparameter configurations such as batch ordering or weight decay yields models suitable for averaging, sharing identical sparse connectivity by design.Averaging these models significantly enhances generalization and OOD performance over their individual counterparts.Building on this, we introduce SPARSE MODEL SOUPS (SMS), a novel method for merging sparse models by initiating each prune-retrain cycle with the averaged model from the previous phase.SMS preserves sparsity, exploits sparse network benefits, is modular and fully parallelizable, and substantially improves IMP's performance.We further demonstrate that SMS can be adapted to enhance state-of-the-art pruning-during-training approaches. | SPARSE MODEL SOUPS: A RECIPE FOR IMPROVED PRUNING VIA MODEL AVERAGING |
d249097923 | The vulnerability of deep neural networks to adversarial examples has drawn tremendous attention from the community. Three approaches, optimizing standard objective functions, exploiting attention maps, and smoothing decision surfaces, are commonly used to craft adversarial examples. By tightly integrating the three approaches, we propose a new and simple algorithm named Transferable Attack based on Integrated Gradients (TAIG) in this paper, which can find highly transferable adversarial examples for black-box attacks. Unlike previous methods using multiple computational terms or combining with other methods, TAIG integrates the three approaches into one single term. Two versions of TAIG that compute their integrated gradients on a straight-line path and a random piecewise linear path are studied. Both versions offer strong transferability and can seamlessly work together with the previous methods. Experimental results demonstrate that TAIG outperforms the state-of-the-art methods. 1 | TRANSFERABLE ADVERSARIAL ATTACK BASED ON INTEGRATED GRADIENTS |
d252668422 | We consider two biologically plausible structures, the Spiking Neural Network (SNN) and the self-attention mechanism. The former offers an energy-efficient and event-driven paradigm for deep learning, while the latter has the ability to capture feature dependencies, enabling Transformer to achieve good performance. It is intuitively promising to explore the marriage between them. In this paper, we consider leveraging both self-attention capability and biological properties of SNNs, and propose a novel Spiking Self Attention (SSA) as well as a powerful framework, named Spiking Transformer (Spikformer). The SSA mechanism in Spikformer models the sparse visual feature by using spike-form Query, Key, and Value without softmax. Since its computation is sparse and avoids multiplication, SSA is efficient and has low computational energy consumption. It is shown that Spikformer with SSA can outperform the state-of-the-art SNNs-like frameworks in image classification on both neuromorphic and static datasets. Spikformer (66.3M parameters) with comparable size to SEW-ResNet-152 (60.2M, 69.26%) can achieve 74.81% top1 accuracy on ImageNet using 4 time steps, which is the state-of-the-art in directly trained SNNs models. Codes will be avaiable at Spikformer. | SPIKFORMER: WHEN SPIKING NEURAL NETWORK MEETS TRANSFORMER |
d249431867 | We consider the off-policy evaluation problem of reinforcement learning using deep convolutional neural networks. We analyze the deep fitted Q-evaluation method for estimating the expected cumulative reward of a target policy, when the data are generated from an unknown behavior policy. We show that, by choosing network size appropriately, one can leverage any low-dimensional manifold structure in the Markov decision process and obtain a sample-efficient estimator without suffering from the curse of high data ambient dimensionality. Specifically, we establish a sharp error bound for fitted Q-evaluation, which depends on the intrinsic dimension of the state-action space, the smoothness of Bellman operator, and a function class-restricted χ 2 -divergence. It is noteworthy that the restricted χ 2 -divergence measures the behavior and target policies' mismatch in the function space, which can be small even if the two policies are not close to each other in their tabular forms. We also develop a novel approximation result for convolutional neural networks in Q-function estimation. Numerical experiments are provided to support our theoretical analysis. * Xiang Ji, Minshuo Chen and Mengdi Wang are affiliated with Princeton University; Tuo Zhao is affiliated with Georgia Tech. | Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks |
d14096841 | We present an approach to training neural networks to generate sequences using actor-critic methods from reinforcement learning (RL). Current log-likelihood training methods are limited by the discrepancy between their training and testing modes, as models must generate tokens conditioned on their previous guesses rather than the ground-truth tokens. We address this problem by introducing a critic network that is trained to predict the value of an output token, given the policy of an actor network. This results in a training procedure that is much closer to the test phase, and allows us to directly optimize for a task-specific score such as BLEU. Crucially, since we leverage these techniques in the supervised learning setting rather than the traditional RL setting, we condition the critic network on the ground-truth output. We show that our method leads to improved performance on both a synthetic task, and for German-English machine translation. Our analysis paves the way for such methods to be applied in natural language generation tasks, such as machine translation, caption generation, and dialogue modelling. * CIFAR Senior Fellow † CIFAR Fellow Published as a conference paper at ICLR 2017 from a sampled prediction lead to a high task-specific score, such as BLEU(Papineni et al., 2002)or ROUGE (Lin & Hovy, 2003).In this work, we propose and study an alternative procedure for training sequence prediction networks that aims to directly improve their test time metrics (which are typically not the log-likelihood). In particular, we train an additional network called the critic to output the value of each token, which we define as the expected task-specific score that the network will receive if it outputs the token and continues to sample outputs according to its probability distribution. Furthermore, we show how the predicted values can be used to train the main sequence prediction network, which we refer to as the actor. The theoretical foundation of our method is that, under the assumption that the critic computes exact values, the expression that we use to train the actor is an unbiased estimate of the gradient of the expected task-specific score.Our approach draws inspiration and borrows the terminology from the field of reinforcement learning (RL)(Sutton & Barto, 1998), in particular from the actor-critic approach(Sutton, 1984;Sutton et al., 1999;Barto et al., 1983). RL studies the problem of acting efficiently based only on weak supervision in the form of a reward given for some of the agent's actions. In our case, the reward is analogous to the task-specific score associated with a prediction. However, the tasks we consider are those of supervised learning, and we make use of this crucial difference by allowing the critic to use the ground-truth answer as an input. In other words, the critic has access to a sequence of expert actions that are known to lead to high (or even optimal) returns. To train the critic, we adapt the temporal difference methods from the RL literature(Sutton, 1988)to our setup. While RL methods with non-linear function approximators are not new(Tesauro, 1994;Miller et al., 1995), they have recently surged in popularity, giving rise to the field of 'deep RL'(Mnih et al., 2015). We show that some of the techniques recently developed in deep RL, such as having a target network, may also be beneficial for sequence prediction.The contributions of the paper can be summarized as follows: 1) we describe how RL methodology like the actor-critic approach can be applied to supervised learning problems with structured outputs; and 2) we investigate the performance and behavior of the new method on both a synthetic task and a real-world task of machine translation, demonstrating the improvements over maximum-likelihood and REINFORCE brought by the actor-critic training. | AN ACTOR-CRITIC ALGORITHM FOR SEQUENCE PREDICTION |
d209460718 | Data augmentation (DA) has been widely utilized to improve generalization in training deep neural networks. Recently, human-designed data augmentation has been gradually replaced by automatically learned augmentation policy. Through finding the best policy in well-designed search space of data augmentation, Au-toAugment (Cubuk et al., 2018) can significantly improve validation accuracy on image classification tasks. However, this approach is not computationally practical for large-scale problems. In this paper, we develop an adversarial method to arrive at a computationally-affordable solution called Adversarial AutoAugment, which can simultaneously optimize target related object and augmentation policy search loss. The augmentation policy network attempts to increase the training loss of a target network through generating adversarial augmentation policies, while the target network can learn more robust features from harder examples to improve the generalization. In contrast to prior work, we reuse the computation in target network training for policy evaluation, and dispense with the retraining of the target network. Compared to AutoAugment, this leads to about 12× reduction in computing cost and 11× shortening in time overhead on ImageNet. We show experimental results of our approach on CIFAR-10/CIFAR-100, ImageNet, and demonstrate significant performance improvements over state-of-the-art. On CIFAR-10, we achieve a top-1 test error of 1.36%, which is the currently best performing single model. On ImageNet, we achieve a leading performance of top-1 accuracy 79.40% on ResNet-50 and 80.00% on ResNet-50-D without extra data. | ADVERSARIAL AUTOAUGMENT |
d3525232 | We introduce a new dataset of logical entailments for the purpose of measuring models' ability to capture and exploit the structure of logical expressions against an entailment prediction task. We use this task to compare a series of architectures which are ubiquitous in the sequence-processing literature, in addition to a new model class-PossibleWorldNets-which computes entailment as a "convolution over possible worlds". Results show that convolutional networks present the wrong inductive bias for this class of problems relative to LSTM RNNs, treestructured neural networks outperform LSTM RNNs due to their enhanced ability to exploit the syntax of logic, and PossibleWorldNets outperform all benchmarks. * Equal contribution.Published as a conference paper at ICLR 2018 * We aim to release the dataset used for experiments, and the code used to generate it according to the constraints discussed in this paper, upon publication of the paper. † E.g., bag-of-words baselines that cannot look at the structure of A and B, or baselines that ignore A and only look at B. | CAN NEURAL NETWORKS UNDERSTAND LOGICAL ENTAILMENT? |
d2103669 | This article presents the prediction difference analysis method for visualizing the response of a deep neural network to a specific input. When classifying images, the method highlights areas in a given input image that provide evidence for or against a certain class. It overcomes several shortcoming of previous methods and provides great additional insight into the decision making process of classifiers. Making neural network decisions interpretable through visualization is important both to improve models and to accelerate the adoption of black-box classifiers in application areas such as medicine. We illustrate the method in experiments on natural images (ImageNet data), as well as medical images (MRI brain scans). | VISUALIZING DEEP NEURAL NETWORK DECISIONS: PREDICTION DIFFERENCE ANALYSIS |
d220280819 | We introduce Adaptive Procedural Task Generation (APT-Gen), an approach for progressively generating a sequence of tasks as curricula to facilitate reinforcement learning in hard-exploration problems. At the heart of our approach, a task generator learns to create tasks via a black-box procedural generation module by adaptively sampling from the parameterized task space. To enable curriculum learning in the absence of a direct indicator of learning progress, the task generator is trained by balancing the agent's expected return in the generated tasks and their similarities to the target task. Through adversarial training, the similarity between the generated tasks and the target task is adaptively estimated by a task discriminator defined on the agent's behaviors. In this way, our approach can efficiently generate tasks of rich variations for target tasks of unknown parameterization or not covered by the predefined task space. Experiments demonstrate the effectiveness of our approach through quantitative and qualitative analysis in various scenarios.Preprint. Under review. | Adaptive Procedural Task Generation for Hard-Exploration Problems |
d249395483 | Past research on interactive decision making problems (bandits, reinforcement learning, etc.) mostly focuses on the minimax regret that measures the algorithm's performance on the hardest instance. However, an ideal algorithm should adapt to the complexity of a particular problem instance and incur smaller regrets on easy instances than worst-case instances. In this paper, we design the first asymptotic instance-optimal algorithm for general interactive decision making problems with finite number of decisions under mild conditions. On every instance f , our algorithm outperforms all consistent algorithms (those achieving non-trivial regrets on all instances), and has asymptotic regret C(f ) ln n, where C(f ) is an exact characterization of the complexity of f . The key step of the algorithm involves hypothesis testing with active data collection. It computes the most economical decisions with which the algorithm collects observations to test whether an estimated instance is indeed correct; thus, the complexity C(f ) is the minimum cost to test the instance f against other instances. Our results, instantiated on concrete problems, recover the classical gap-dependent bounds for multi-armed bandits [Lai et al., 1985] and prior works on linear bandits [Lattimore and Szepesvari, 2017], and improve upon the previous best instance-dependent upper bound [Xu et al., 2021] for reinforcement learning. , et al. Magnetic control of tokamak plasmas through deep reinforcement learning. Nature, 602(7897):414-419, 2022. Kefan Dong, Jiaqi Yang, and Tengyu Ma. Provable model-based nonlinear bandit and reinforcement learning: Shelve optimism, embrace virtual curvature. arXiv preprint arXiv:2102.04168, 2021. Dylan Foster and Alexander Rakhlin. Beyond UCB: Optimal and efficient contextual bandits with regression oracles. In . Instance-dependent complexity of contextual bandits and reinforcement learning: A disagreement-based perspective. arXiv preprint arXiv:2010.03104, 2020. Dylan J Foster, Sham M Kakade, Jian Qian, and Alexander Rakhlin. The statistical complexity of interactive decision making. arXiv preprint arXiv:2112.13487, 2021. Manuel Gil, Fady Alajaji, and Tamas Linder. Rényi divergence measures for commonly used univariate continuous distributions. Information Sciences, 249:124-131, 2013.Todd L Graves and Tze Leung Lai. Asymptotically efficient adaptive choice of control laws incontrolled markov chains. | Asymptotic Instance-Optimal Algorithms for Interactive Decision Making |
d3529936 | Implicit models, which allow for the generation of samples but not for point-wise evaluation of probabilities, are omnipresent in real world problems tackled by machine learning and a hot topic of current research. Some examples include data simulators that are widely used in engineering and scientific research, generative adversarial networks (GANs) for image synthesis, and hot-off-the-press approximate inference techniques relying on implicit distributions. The majority of existing approaches to learning implicit models rely on approximating the intractable distribution or optimisation objective for gradient-based optimisation, which is liable to produce inaccurate updates and thus poor models. This paper alleviates the need for such approximations by proposing the Stein gradient estimator, which directly estimates the score function of the implicitly defined distribution. The efficacy of the proposed estimator is empirically demonstrated by examples that include meta-learning for approximate inference, and entropy regularised GANs that provide improved sample diversities. | Gradient Estimators for Implicit Models |
d232478335 | This work aims to empirically clarify a recently discovered perspective that label smoothing is incompatible with knowledge distillation(Müller et al., 2019). We begin by introducing the motivation behind on how this incompatibility is raised, i.e., label smoothing erases relative information between teacher logits. We provide a novel connection on how label smoothing affects distributions of semantically similar and dissimilar classes. Then we propose a metric to quantitatively measure the degree of erased information in sample's representation. After that, we study its one-sidedness and imperfection of the incompatibility view through massive analyses, visualizations and comprehensive experiments on Image Classification, Binary Networks, and Neural Machine Translation. Finally, we broadly discuss several circumstances wherein label smoothing will indeed lose its effectiveness. | IS LABEL SMOOTHING TRULY INCOMPATIBLE WITH KNOWLEDGE DISTILLATION: AN EMPIRICAL STUDY |
d227254803 | We study a general class of contextual bandits, where each context-action pair is associated with a raw feature vector, but the reward generating function is unknown. We propose a novel learning algorithm that transforms the raw feature vector using the last hidden layer of a deep ReLU neural network (deep representation learning), and uses an upper confidence bound (UCB) approach to explore in the last linear layer (shallow exploration). We prove that under standard assumptions, our proposed algorithm achieves O( √ T ) finite-time regret, where T is the learning time horizon. Compared with existing neural contextual bandit algorithms, our approach is computationally much more efficient since it only needs to explore in the last layer of the deep neural network. | Neural Contextual Bandits with Deep Representation and Shallow Exploration |
d208857488 | A major component of overfitting in model-free reinforcement learning (RL) involves the case where the agent may mistakenly correlate reward with certain spurious features from the observations generated by the Markov Decision Process (MDP). We provide a general framework for analyzing this scenario, which we use to design multiple synthetic benchmarks from only modifying the observation space of an MDP. When an agent overfits to different observation spaces even if the underlying MDP dynamics is fixed, we term this observational overfitting. Our experiments expose intriguing properties especially with regards to implicit regularization, and also corroborate results from previous works in RL generalization and supervised learning (SL). * Work partially performed as an OpenAI Fellow. † Work performed during the Google AI Residency Program. http://g.co/airesidency 1 | Observational Overfitting in Reinforcement Learning |
d246822414 | The current methods for learning representations with auto-encoders almost exclusively employ vectors as the latent representations. In this work, we propose to employ a tensor product structure for this purpose. This way, the obtained representations are naturally disentangled. In contrast to the conventional variations methods, which are targeted toward normally distributed features, the latent space in our representation is distributed uniformly over a set of unit circles. We argue that the torus structure of the latent space captures the generative factors effectively. We employ recent tools for measuring unsupervised disentanglement, and in an extensive set of experiments demonstrate the advantage of our method in terms of disentanglement, completeness, and informativeness. The code for our proposed method is available at https://github.com/rotmanmi/ Unsupervised-Disentanglement-Torus. | UNSUPERVISED DISENTANGLEMENT WITH TENSOR PRODUCT REPRESENTATIONS ON THE TORUS |
d245117682 | In many practical applications of RL, it is expensive to observe state transitions from the environment. For example, in the problem of plasma control for nuclear fusion, computing the next state for a given state-action pair requires querying an expensive transition function which can lead to many hours of computer simulation or dollars of scientific research. Such expensive data collection prohibits application of standard RL algorithms which usually require a large number of observations to learn. In this work, we address the problem of efficiently learning a policy while making a minimal number of state-action queries to the transition function. In particular, we leverage ideas from Bayesian optimal experimental design to guide the selection of state-action queries for efficient learning. We propose an acquisition function that quantifies how much information a state-action pair would provide about the optimal solution to a Markov decision process. At each iteration, our algorithm maximizes this acquisition function, to choose the most informative state-action pair to be queried, thus yielding a data-efficient RL approach. We experiment with a variety of simulated continuous control problems and show that our approach learns an optimal policy with up to 5 -1, 000× less data than modelbased RL baselines and 10 3 -10 5 × less data than model-free RL baselines. We also provide several ablated comparisons which point to substantial improvements arising from the principled method of obtaining data. | AN EXPERIMENTAL DESIGN PERSPECTIVE ON MODEL-BASED REINFORCEMENT LEARNING |
d256390058 | A temporal point process (TPP) is a stochastic process where its realization is a sequence of discrete events in time. Recent work in TPPs model the process using a neural network in a supervised learning framework, where a training set is a collection of all the sequences. In this work, we propose to train TPPs in a meta learning framework, where each sequence is treated as a different task, via a novel framing of TPPs as neural processes (NPs). We introduce context sets to model TPPs as an instantiation of NPs. Motivated by attentive NP, we also introduce local history matching to help learn more informative features. We demonstrate the potential of the proposed method on popular public benchmark datasets and tasks, and compare with state-of-the-art TPP methods. | META TEMPORAL POINT PROCESSES |
d67856276 | Multilingual machine translation, which translates multiple languages with a single model, has attracted much attention due to its efficiency of offline training and online serving. However, traditional multilingual translation usually yields inferior accuracy compared with the counterpart using individual models for each language pair, due to language diversity and model capacity limitations. In this paper, we propose a distillation-based approach to boost the accuracy of multilingual machine translation. Specifically, individual models are first trained and regarded as teachers, and then the multilingual model is trained to fit the training data and match the outputs of individual models simultaneously through knowledge distillation. Experiments on IWSLT, WMT and Ted talk translation datasets demonstrate the effectiveness of our method. Particularly, we show that one model is enough to handle multiple languages (up to 44 languages in our experiment), with comparable or even better accuracy than individual models. * Authors contribute equally to this work. | MULTILINGUAL NEURAL MACHINE TRANSLATION WITH KNOWLEDGE DISTILLATION |
d235293986 | A probabilistic classifier with reliable predictive uncertainties i) fits successfully to the target domain data, ii) provides calibrated class probabilities in difficult regions of the target domain (e.g. class overlap), and iii) accurately identifies queries coming out of the target domain and rejects them. We introduce an original combination of Evidential Deep Learning, Neural Processes, and Neural Turing Machines capable of providing all three essential properties mentioned above for total uncertainty quantification. We observe our method on five classification tasks to be the only one that can excel all three aspects of total calibration with a single standalone predictor. Our unified solution delivers an implementation-friendly and compute efficient recipe for safety clearance and provides intellectual economy to an investigation of algorithmic roots of epistemic awareness in deep neural nets. | EVIDENTIAL TURING PROCESSES |
d6610705 | We introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator. This allows training to be adjusted between using the optimal discriminator in the generator's objective, which is ideal but infeasible in practice, and using the current value of the discriminator, which is often unstable and leads to poor solutions. We show how this technique solves the common problem of mode collapse, stabilizes training of GANs with complex recurrent generators, and increases diversity and coverage of the data distribution by the generator. * Work done as a member of the Google Brain Residency program (g.co/brainresidency) † Work completed as part of a Google Brain internship | UNROLLED GENERATIVE ADVERSARIAL NETWORKS |
d3557557 | What makes humans so good at solving seemingly complex video games? Unlike computers, humans bring in a great deal of prior knowledge about the world, enabling efficient decision making. This paper investigates the role of human priors for solving video games. Given a sample game, we conduct a series of ablation studies to quantify the importance of various priors on human performance. We do this by modifying the video game environment to systematically mask different types of visual information that could be used by humans as priors. We find that removal of some prior knowledge causes a drastic degradation in the speed with which human players solve the game, e.g. from 2 minutes to over 20 minutes. Furthermore, our results indicate that general priors, such as the importance of objects and visual consistency, are critical for efficient game-play. Videos and the game manipulations are available at https://rach0012. github.io/humanRL_website/. | Investigating Human Priors for Playing Video Games |
d264438909 | Exposing meaningful and interpretable neural interactions is critical to understanding neural circuits.Inferred neural interactions from neural signals primarily reflect functional interactions.In a long experiment, subject animals may experience different stages defined by the experiment, stimuli, or behavioral states, and hence functional interactions can change over time.To model dynamically changing functional interactions, prior work employs state-switching generalized linear models with hidden Markov models (i.e., HMM-GLMs).However, we argue they lack biological plausibility, as functional interactions are shaped and confined by the underlying anatomical connectome.Here, we propose a novel prior-informed state-switching GLM.We introduce both a Gaussian prior and a one-hot prior over the GLM in each state.The priors are learnable.We will show that the learned prior should capture the state-constant interaction, shedding light on the underlying anatomical connectome and revealing more likely physical neuron interactions.The state-dependent interaction modeled by each GLM offers traceability to capture functional variations across multiple brain states.Our methods effectively recover true interaction structures in simulated data, achieve the highest predictive likelihood with real neural datasets, and render interaction structures and hidden states more interpretable when applied to real neural data. | ONE-HOT GENERALIZED LINEAR MODEL FOR SWITCHING BRAIN STATE DISCOVERY |
d235417023 | The adversarial vulnerability of deep neural networks has attracted significant attention in machine learning. From a causal viewpoint, adversarial attacks can be considered as a specific type of distribution change on natural data. As causal reasoning has an instinct for modeling distribution change, we propose to incorporate causality into mitigating adversarial vulnerability. However, causal formulations of the intuition of adversarial attack and the development of robust DNNs are still lacking in the literature. To bridge this gap, we construct a causal graph to model the generation process of adversarial examples and define the adversarial distribution to formalize the intuition of adversarial attacks. From a causal perspective, we find that the label is spuriously correlated with the style (content-independent) information when an instance is given. The spurious correlation implies that the adversarial distribution is constructed via making the statistical conditional association between style information and labels drastically different from that in natural distribution. Thus, DNNs that fit the spurious correlation are vulnerable to the adversarial distribution. Inspired by the observation, we propose the adversarial distribution alignment method to eliminate the difference between the natural distribution and the adversarial distribution. Extensive experiments demonstrate the efficacy of the proposed method. Our method can be seen as the first attempt to leverage causality for mitigating adversarial vulnerability. * Work done during an internship at Hong Kong Baptist University. | Adversarial Robustness through the Lens of Causality |
d49473438 | While Generative Adversarial Networks (GANs) have empirically produced impressive results on learning complex real-world distributions, recent work has shown that they suffer from lack of diversity or mode collapse. The theoretical work of Arora et al.[2]suggests a dilemma about GANs' statistical properties: powerful discriminators cause overfitting, whereas weak discriminators cannot detect mode collapse.In contrast, we show in this paper that GANs can in principle learn distributions in Wasserstein distance (or KL-divergence in many cases) with polynomial sample complexity, if the discriminator class has strong distinguishing power against the particular generator class (instead of against all possible generators). For various generator classes such as mixture of Gaussians, exponential families, and invertible and injective neural networks generators, we design corresponding discriminators (which are often neural nets of specific architectures) such that the Integral Probability Metric (IPM) induced by the discriminators can provably approximate the Wasserstein distance and/or KL-divergence. This implies that if the training is successful, then the learned distribution is close to the true distribution in Wasserstein distance or KL divergence, and thus cannot drop modes. Our preliminary experiments show that on synthetic datasets the test IPM is well correlated with KL divergence, indicating that the lack of diversity may be caused by the sub-optimality in optimization instead of statistical inefficiency.1 Informally, if most of the modes of p are ε-far away from each other, then as long as W1(p, q) ε, q has to contain most of the modes of p.2We also note that the third inequality can hold for all p, q as long as F is a subset of Lipschitz functions.3 the generator has restricted approximability 3 . We show this discriminator class guarantees that W 1 (p, q) 2 W F (p, q) W 1 (p, q) where here we hide polynomial dependencies on relevant parameters (Theorem 4.3). We remark that such networks can also produce an exponentially large number of modes due to the non-linearities, and our results imply that if W F (p, q) is small, then most of these exponential modes will show up in the learned distribution q. An intermediate step towards the result above is a very simple but powerful observation ([39, Proposition 2.9] or see Lemma 4.1): if the log density of every distribution p ∈ G can be approximated by the family of discriminators in F, then the KL-divergence D kl (p q) is bounded from above byW F (p, q). Indeed, the log-density of a neural net generator can be computed by another neural network as long as each weight matrix and the activation function is invertible. With this lemma, it remains to show that KL is bounded below from by Wasserstein distance, which follows from the transportation inequalities for sub-Gaussian measures such as the Bobkov-Götze and Gozlan theorems ([34], restated in Theorem D.1).One limitation of the invertibility assumption is that it only produces distributions supported on the entire space. The distribution of natural images is often believed to reside approximately on a low-dimensional manifold. When the distribution p have a Lebesgue measure-zero support, the KL-divergence (or the reverse KL-divergence) is infinity unless the support of the estimated distribution coincides with the support of p. 4 Therefore, the KL-divergence is fundamentally not the proper measurement of the statistical distance for the cases where both p and q have lowdimensional supports.The crux of the technical part of the paper is to establish the approximation of Waserstein distance by IPMs for generators with low-dimensional supports. We will show that some variant of IPM can still be sandwiched by Wasserstein distance as in form of (5) without relating to KL-divergence (Theorem 4.5). This demonstrates the advantage of GANs over MLE approach on learning distributions with low-dimensional supports. As the main proof technique, we develop tools for approximating the log-density of a smoothed neural network generator.In Section 5, we demonstrate in synthetic and controlled experiments that the IPM correlates with KL-divergence for the invertible generator family (where computation of KL is feasible). The theory suggests the possibility that when the KL-divergence is not measurable in more complicated settings, the test IPM could serve as a candidate alternative for measuring the diversity and quality of the learned distribution. We also remark that on real datasets, often the optimizer is tuned to carefully balance the learning of generators and discriminators, and therefore the reported training loss is often not the test IPM (which requires optimizing the discriminator until optimality.) Anecdotally, the distributions learned by GANs can often be distinguished by a well-trained discriminator from the data distribution, which suggests that the IPM is not well-optimized (See[20]for analysis of for the original GANs formulation.). The authors conjecture that the lack of diversity in real experiments may be caused by the sub-optimality in optimization, instead of statistical inefficiency.Related workEmpirical proxy tests for diversity, memorization, and generalization were developed such as interpolation between images [26], semantic combination of images via arithmetic in latent space [4], classification tests [29], etc. These results by and large indicate that while "memorization" is not | Approximability of Discriminators Implies Diversity in GANs |
d54101493 | Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more important role of image textures. We here put these conflicting hypotheses to a quantitative test by evaluating CNNs and human observers on images with a texture-shape cue conflict. We show that ImageNettrained CNNs are strongly biased towards recognising textures rather than shapes, which is in stark contrast to human behavioural evidence and reveals fundamentally different classification strategies. We then demonstrate that the same standard architecture (ResNet-50) that learns a texture-based representation on ImageNet is able to learn a shape-based representation instead when trained on 'Stylized-ImageNet', a stylized version of ImageNet. This provides a much better fit for human behavioural performance in our well-controlled psychophysical lab setting (nine experiments totalling 48,560 psychophysical trials across 97 observers) and comes with a number of unexpected emergent benefits such as improved object detection performance and previously unseen robustness towards a wide range of image distortions, highlighting advantages of a shape-based representation. de Beeck. Deep neural networks as a computational model for human shape sensitivity. DiCarlo. Performance-optimized hierarchical models predict neural responses in higher visual cortex. | IMAGENET-TRAINED CNNS ARE BIASED TOWARDS TEXTURE; INCREASING SHAPE BIAS IMPROVES ACCURACY AND ROBUSTNESS |
d56657799 | We present a simple and general method to train a single neural network executable at different widths 1 , permitting instant and adaptive accuracy-efficiency trade-offs at runtime. Instead of training individual networks with different width configurations, we train a shared network with switchable batch normalization. At runtime, the network can adjust its width on the fly according to on-device benchmarks and resource constraints, rather than downloading and offloading different models. Our trained networks, named slimmable neural networks, achieve similar (and in many cases better) ImageNet classification accuracy than individually trained models of MobileNet v1, MobileNet v2, ShuffleNet and ResNet-50 at different widths respectively. We also demonstrate better performance of slimmable models compared with individual ones across a wide range of applications including COCO bounding-box object detection, instance segmentation and person keypoint detection without tuning hyper-parameters. Lastly we visualize and discuss the learned features of slimmable networks. Code and models are available at: | SLIMMABLE NEURAL NETWORKS |
d254043945 | Existing object detection methods are bounded in a fixed-set vocabulary by costly labeled data. When dealing with novel categories, the model has to be retrained with more bounding box annotations. Natural language supervision is an attractive alternative for its annotation-free attributes and broader object concepts.However, learning open-vocabulary object detection from language is challenging since image-text pairs do not contain fine-grained object-language alignments. Previous solutions rely on either expensive grounding annotations or distilling classification-oriented vision models. In this paper, we propose a novel openvocabulary object detection framework directly learning from image-text pair data. We formulate object-language alignment as a set matching problem between a set of image region features and a set of word embeddings. It enables us to train an open-vocabulary object detector on image-text pairs in a much simple and effective way. Extensive experiments on two benchmark datasets, COCO and LVIS, demonstrate our superior performance over the competing approaches on novel categories, e.g. achieving 32.0% mAP on COCO and 21.7% mask mAP on LVIS. Code is available at: https://github.com/clin1223/VLDet. * This work was performed while Chuang Lin (chuang.lin@monash.edu) worked as an intern at ByteDance. | LEARNING OBJECT-LANGUAGE ALIGNMENTS FOR OPEN-VOCABULARY OBJECT DETECTION |
d252816031 | Meta-training, which fine-tunes the language model (LM) on various downstream tasks by maximizing the likelihood of the target label given the task instruction and input instance, has improved the zero-shot task generalization performance. However, meta-trained LMs still struggle to generalize to challenging tasks containing novel labels unseen during meta-training. In this paper, we propose FLIPPED LEARNING, an alternative method of meta-training which trains the LM to generate the task instruction given the input instance and label. During inference, the LM trained with FLIPPED LEARNING, referred to as FLIPPED, selects the label option that is most likely to generate the task instruction. On 14 tasks of the BIG-bench benchmark, the 11B-sized FLIPPED outperforms zero-shot T0-11B (Sanh et al., 2021) and even a 16 times larger 3-shot GPT-3 (175B) (Brown et al., 2020) on average by 8.4% and 9.7% points, respectively. FLIPPED gives particularly large improvements on unseen labels, outperforming T0-11B by up to +20% average F1 score. This indicates that the strong task generalization of FLIPPED comes from improved generalization to novel labels. We release our code at https://github.com/seonghyeonye/Flipped-Learning. | GUESS THE INSTRUCTION! FLIPPED LEARNING MAKES LANGUAGE MODELS STRONGER ZERO-SHOT LEARNERS |
d253707922 | How should we intervene on an unknown structural causal model to maximize a downstream variable of interest? This optimization of the output of a system of interconnected variables, also known as causal Bayesian optimization (CBO), has important applications in medicine, ecology, and manufacturing. Standard Bayesian optimization algorithms fail to effectively leverage the underlying causal structure. Existing CBO approaches assume noiseless measurements and do not come with guarantees. We propose model-based causal Bayesian optimization (MCBO), an algorithm that learns a full system model instead of only modeling intervention-reward pairs. MCBO propagates epistemic uncertainty about the causal mechanisms through the graph and trades off exploration and exploitation via the optimism principle. We bound its cumulative regret, and obtain the first non-asymptotic bounds for CBO. Unlike in standard Bayesian optimization, our acquisition function cannot be evaluated in closed form, so we show how the reparameterization trick can be used to apply gradient-based optimizers. Empirically we find that MCBO compares favorably with existing state-of-the-art approaches. | MODEL-BASED CAUSAL BAYESIAN OPTIMIZATION |
d235377198 | We propose a new framework of synthesizing data using deep generative models in a differentially private manner. Within our framework, sensitive data are sanitized with rigorous privacy guarantees in a one-shot fashion, such that training deep generative models is possible without re-using the original data. Hence, no extra privacy costs or model constraints are incurred, in contrast to popular gradient sanitization approaches, which, among other issues, cause degradation in privacy guarantees as the training iteration increases. We demonstrate a realization of our framework by making use of the characteristic function and an adversarial re-weighting objective, which are of independent interest as well. Our proposal has theoretical guarantees of performance, and empirical evaluations on multiple datasets show that our approach outperforms other methods at reasonable levels of privacy. | PEARL: DATA SYNTHESIS VIA PRIVATE EMBEDDINGS AND ADVERSARIAL RECONSTRUCTION LEARNING |
d236912505 | Reasoning about the future -understanding how decisions in the present time affect outcomes in the future -is one of the central challenges for reinforcement learning (RL), especially in highly-stochastic or partially observable environments. While predicting the future directly is hard, in this work we introduce a method that allows an agent to "look into the future" without explicitly predicting it. Namely, we propose to allow an agent, during its training on past experience, to observe what actually happened in the future at that time, while enforcing an information bottleneck to avoid the agent overly relying on this privileged information. This gives our agent the opportunity to utilize rich and useful information about the future trajectory dynamics in addition to the present. Our method, Policy Gradients Incorporating the Future (PGIF), is easy to implement and versatile, being applicable to virtually any policy gradient algorithm. We apply our proposed method to a number of off-the-shelf RL algorithms and show that PGIF is able to achieve higher reward faster in a variety of online and offline RL domains, as well as sparse-reward and partially observable environments. | Policy Gradients Incorporating the Future |
d261823054 | Markov processes are widely used mathematical models for describing dynamic systems in various fields.However, accurately simulating large-scale systems at long time scales is computationally expensive due to the short time steps required for accurate integration.In this paper, we introduce an inference process that maps complex systems into a simplified representational space and models large jumps in time.To achieve this, we propose Time-lagged Information Bottleneck (T-IB), a principled objective rooted in information theory, which aims to capture relevant temporal features while discarding high-frequency information to simplify the simulation task and minimize the inference error.Our experiments demonstrate that T-IB learns information-optimal representations for accurately modeling the statistical properties and dynamics of the original process at a selected time lag, outperforming existing time-lagged dimensionality reduction methods. | Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck |
d225067567 | We re-evaluate the standard practice of sharing weights between input and output embeddings in state-of-the-art pre-trained language models. We show that decoupled embeddings provide increased modeling flexibility, allowing us to significantly improve the efficiency of parameter allocation in the input embedding of multilingual models. By reallocating the input embedding parameters in the Transformer layers, we achieve dramatically better performance on standard natural language understanding tasks with the same number of parameters during fine-tuning. We also show that allocating additional capacity to the output embedding provides benefits to the model that persist through the fine-tuning stage even though the output embedding is discarded after pre-training. Our analysis shows that larger output embeddings prevent the model's last layers from overspecializing to the pre-training task and encourage Transformer representations to be more general and more transferable to other tasks and languages. Harnessing these findings, we are able to train models that achieve strong performance on the XTREME benchmark without increasing the number of parameters at the fine-tuning stage. * equal contribution † Work done as a member of the Google AI Residency Program. 1 Output embedding is sometimes referred to as "output weights", i.e., the weight matrix in the output projection in a language model. 2 We focus on encoder-only models, and do not consider encoder-decoder models like T5 (Raffel et al., 2020) where none of the embedding matrices are discarded after pre-training. Output embeddings may also be | RETHINKING EMBEDDING COUPLING IN PRE-TRAINED LANGUAGE MODELS |
d256358895 | Although many methods have been proposed to estimate attributions of input variables, there still exists a significant theoretical flaw in masking-based attribution methods, i.e. it is hard to examine whether the masking method faithfully represents the absence of input variables. Specifically, for masking-based attributions, setting an input variable to the baseline value is a typical way of representing the absence of the variable. However, there are no studies investigating how to represent the absence of input variables and verify the faithfulness of baseline values. Therefore, we revisit the feature representation of a DNN in terms of causality, and propose to use causal patterns to examine whether the masking method faithfully removes information encoded in input variables. More crucially, it is proven that the causality can be explained as the elementary rationale of the Shapley value. Furthermore, we define the optimal baseline value from the perspective of causality, and we propose a method to learn the optimal baseline value. Experimental results have demonstrated the effectiveness of our method. | CAN WE FAITHFULLY REPRESENT ABSENCE STATES TO COMPUTE SHAPLEY VALUES ON A DNN? |
d259108558 | In many domains, autoregressive models can attain high likelihood on the task of predicting the next observation. However, this maximum-likelihood (MLE) objective does not necessarily match a downstream use-case of autoregressively generating high-quality sequences. The MLE objective weights sequences proportionally to their frequency under the data distribution, with no guidance for the model's behaviour out of distribution (OOD): leading to compounding error during autoregressive generation. In order to address this compounding error problem, we formulate sequence generation as an imitation learning (IL) problem. This allows us to minimize a variety of divergences between the distribution of sequences generated by an autoregressive model and sequences from a dataset, including divergences with weight on OOD generated sequences. The IL framework also allows us to incorporate backtracking by introducing a backspace action into the generation process. This further mitigates the compounding error problem by allowing the model to revert a sampled token if it takes the sequence OOD. Our resulting method, SequenceMatch, can be implemented without adversarial training or architectural changes. We identify the SequenceMatch-χ 2 divergence as a more suitable training objective for autoregressive models which are used for generation. We show that empirically, SequenceMatch training leads to improvements over MLE on text generation with language models.Preprint. Under review. | SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking |
d211003696 | Markov Logic Networks (MLNs), which elegantly combine logic rules and probabilistic graphical models, can be used to address many knowledge graph problems. However, inference in MLN is computationally intensive, making the industrialscale application of MLN very difficult. In recent years, graph neural networks (GNNs) have emerged as efficient and effective tools for large-scale graph problems. Nevertheless, GNNs do not explicitly incorporate prior logic rules into the models, and may require many labeled examples for a target task. In this paper, we explore the combination of MLNs and GNNs, and use graph neural networks for variational inference in MLN. We propose a GNN variant, named ExpressGNN, which strikes a nice balance between the representation power and the simplicity of the model. Our extensive experiments on several benchmark datasets demonstrate that ExpressGNN leads to effective and efficient probabilistic logic reasoning.Published as a conference paper at ICLR 2020 Variational EM Posterior Encoding Likelihood Decoding GNN MLN Knowledge Graph " (⋅) formula potential predicate posterior Figure 1: Overview of our method for combining MLN and GNN using the variational EM framework.• Efficient inference and learning: ExpressGNN can be viewed as the inference network for MLN, which scales up MLN inference to much larger knowledge graph problems.• Combining logic rules and data supervision: ExpressGNN can leverage the prior knowledge encoded in logic rules, as well as the supervision from graph structured data.• Compact and expressive model: ExpressGNN may have small number of parameters, yet it is sufficient to represent mean-field distributions in MLN.• Capability of zero-shot learning: ExpressGNN can deal with the zero-shot learning problem where the target predicate has few or zero labeled instances. | EFFICIENT PROBABILISTIC LOGIC REASONING WITH GRAPH NEURAL NETWORKS |
d259064354 | Motivated by social network analysis and network-based recommendation systems, we study a semi-supervised community detection problem in which the objective is to estimate the community label of a new node using the network topology and partially observed community labels of existing nodes. The network is modeled using a degree-corrected stochastic block model, which allows for severe degree heterogeneity and potentially non-assortative communities. We propose an algorithm that computes a 'structural similarity metric' between the new node and each of the K communities by aggregating labeled and unlabeled data. The estimated label of the new node corresponds to the value of k that maximizes this similarity metric. Our method is fast and numerically outperforms existing semi-supervised algorithms. Theoretically, we derive explicit bounds for the misclassification error and show the efficiency of our method by comparing it with an ideal classifier. Our findings highlight, to the best of our knowledge, the first semi-supervised community detection algorithm that offers theoretical guarantees.Published as a conference paper at ICLR 2023The statistical literature on community detection has mainly focused on the unsupervised setting(Bickel & Chen, 2009;Rohe et al., 2011;Jin, 2015;Gao et al., 2018;Li et al., 2021). The semisupervised setting is less studied. Leng & Ma(2019)offers a comprehensive literature review of semi-supervised community detection algorithms. Liu et al. (2014) and Ji et al. (2016) derive systems of linear equations for the community labels through physics theory, and predict the labels by solving those equations. Zhou et al. (2018) leverages on the belief function to propagate labels across the network, so that one can estimate the label of a node through its belief. Betzel et al. (2018) extracts several patterns in size and structural composition across the known communities and search for similar patterns in the graph. Yang et al. (2015)unifies a number of different community detection algorithms based on non-negative matrix factorization or spectral clustering under the unsupervised setting, and fits them into the semi-supervised scenario by adding various regularization terms to encourage the estimated labels for nodes in L to match with the clustering behavior of their observed labels. However, the existing methods still face challenges. First, many of them employ the heuristic that a node tends to have more edges with nodes in the same community than those in other communities. This is true only when communities are assortative. But non-assortative communities are also seen in real networks(Goldenberg et al., 2010;Betzel et al., 2018); for instance, Facebook users sharing similar restaurant preferences are not necessarily friends of each other. Second, real networks often have severe degree heterogeneity (i.e., the degrees of some nodes can be many times larger than the degrees of other nodes), but most semi-supervised community detection algorithms do not handle degree heterogeneity. Third, the optimization-based algorithms(Yang et al., 2015)solve non-convex problems and face the issue of local minima. Last, to our best knowledge, none of the existing methods have theoretical guarantees. | SEMI-SUPERVISED COMMUNITY DETECTION VIA STRUCTURAL SIMILARITY METRICS |
d252967887 | The low-level sensory and motor signals in deep reinforcement learning, which exist in high-dimensional spaces such as image observations or motor torques, are inherently challenging to understand or utilize directly for downstream tasks. While sensory representations have been extensively studied, the representations of motor actions are still an area of active exploration. Our work reveals that a space containing meaningful action representations emerges when a multi-task policy network takes as inputs both states and task embeddings. Moderate constraints are added to improve its representation ability. Therefore, interpolated or composed embeddings can function as a high-level interface within this space, providing instructions to the agent for executing meaningful action sequences. Empirical results demonstrate that the proposed action representations are effective for intra-action interpolation and inter-action composition with limited or no additional learning. Furthermore, our approach exhibits superior task adaptation ability compared to strong baselines in Mujoco locomotion tasks. Our work sheds light on the promising direction of learning action representations for efficient, adaptable, and composable RL, forming the basis of abstract action planning and the understanding of motor signal space. Project page: https://sites. google.com/view/emergent-action-representation/ * Denotes equal contributions. | SIMPLE EMERGENT ACTION REPRESENTATIONS FROM MULTI-TASK POLICY TRAINING |
d249538510 | Despite recent progress in generative adversarial network (GAN)-based vocoders, where the model generates raw waveform conditioned on acoustic features, it is challenging to synthesize high-fidelity audio for numerous speakers across various recording environments. In this work, we present BigVGAN, a universal vocoder that generalizes well for various out-of-distribution scenarios without fine-tuning. We introduce periodic activation function and anti-aliased representation into the GAN generator, which brings the desired inductive bias for audio synthesis and significantly improves audio quality. In addition, we train our GAN vocoder at the largest scale up to 112M parameters, which is unprecedented in the literature. We identify and address the failure modes in large-scale GAN training for audio, while maintaining high-fidelity output without over-regularization. Our BigVGAN, trained only on clean speech (LibriTTS), achieves the state-of-the-art performance for various zero-shot (out-of-distribution) conditions, including unseen speakers, languages, recording environments, singing voices, music, and instrumental audio. 1 We release our code and model at: https://github.com/NVIDIA/BigVGAN. * Work done during an internship at NVIDIA. † Corresponding authors. 1 Listen to audio samples from BigVGAN at: https://bigvgan-demo.github.io/.Published as a conference paper at ICLR 2023 Scaling up the model size for zero-shot performance is a noticeable trend in text generation (e.g.,Brown et al., 2020)and image synthesis (e.g., Ramesh et al., 2021), but has not been explored in audio synthesis. Although likelihood-based models are found to be easier for scaling among others because of their simple training objective and stable optimization, we build our universal vocoder with large-scale GAN training, because GAN vocoder has the following advantages: i) In contrast to autoregressive or diffusion models, it is fully parallel and requires only one forward pass to generate high-dimensional waveform. ii) In contrast to flow-based models (Prenger et al., 2019), it does not enforce any architectural constraints (e.g., affine coupling layer) that maintain the bijection between latent and data. Such architectural constraints can limit model capacity given the same number of parameters (Ping et al., 2020).In this work, we present BigVGAN, a Big Vocoding GAN that enables high-fidelity out-ofdistribution (OOD) generation without fine-tuning. Specifically, we make the following contributions: 1. We introduce periodic activations into the generator, which provide the desired inductive bias for audio synthesis. Inspired by the methods proposed for other domains (Liu et al., 2020; Sitzmann et al., 2020), we demonstrate the noticeable success of periodic activations in audio synthesis. 2. We propose anti-aliased multi-periodicity composition (AMP) module for modeling complex audio waveform. AMP composes multiple signal components with learnable periodicities and uses low-pass filter to reduce the high-frequency artifacts. 3. We successfully scale BigVGAN up to 112M parameters by fixing the failure modes of large-scale GAN training without regularizing both generator and discriminator. The empirical insights are different fromBrock et al. (2019)in image domain. For example, regularization methods (e.g., Miyato et al., 2018) introduce phase mismatch artifacts in audio synthesis. 4. We demonstrate that BigVGAN-base with 14M parameters outperforms the state-of-the-art neural vocoders with comparable size for both in-distribution and out-of-distribution samples. In particular, BigVGAN with 112M parameters outperforms the state-of-the-art models by a large margin for zero-shot generation at various OOD scenarios, including unseen speakers, novel languages, singing voices, music and instrumental audio in varied unseen recording environments. We organize the rest of the paper as follows. We discuss related work in § 2 and present BigVGAN in § 3. We report empirical results in § 4 and conclude the paper in § 5. speech-to-speech translation with a sequence-to-sequence model. In Interspeech, 2019. | BIGVGAN: A UNIVERSAL NEURAL VOCODER WITH LARGE-SCALE TRAINING |
d258331519 | In this paper, we define, evaluate, and improve the "relay-generalization" performance of reinforcement learning (RL) agents on the out-of-distribution "controllable" states. Ideally, an RL agent that generally masters a task should reach its goal starting from any controllable state of the environment instead of memorizing a small set of trajectories. For example, a self-driving system should be able to take over the control from humans in the middle of driving and must continue to drive the car safely. To practically evaluate this type of generalization, we start the test agent from the middle of other independently well-trained stranger agents' trajectories. With extensive experimental evaluation, we show the prevalence of generalization failure on controllable states from stranger agents. For example, in the Humanoid environment, we observed that a well-trained Proximal Policy Optimization (PPO) agent, with only 3.9% failure rate during regular testing, failed on 81.6% of the states generated by well-trained stranger PPO agents. To improve "relay generalization," we propose a novel method called Self-Trajectory Augmentation (STA), which will reset the environment to the agent's old states according to the Q function during training. After applying STA to the Soft Actor Critic's (SAC) training procedure, we reduced the failure rate of SAC under relay-evaluation by more than three times in most settings without impacting agent performance and increasing the needed number of environment interactions. Our code is available at https://github.com/lan-lc/STA. | CAN AGENTS RUN RELAY RACE WITH STRANGERS? GENERALIZATION OF RL TO OUT-OF-DISTRIBUTION TRAJECTORIES |
d227346855 | The objective of lifelong reinforcement learning (RL) is to optimize agents which can continuously adapt and interact in changing environments. However, current RL approaches fail drastically when environments are non-stationary and interactions are non-episodic. We propose Lifelong Skill Planning (LiSP), an algorithmic framework for non-episodic lifelong RL based on planning in an abstract space of higher-order skills. We learn the skills in an unsupervised manner using intrinsic rewards and plan over the learned skills using a learned dynamics model. Moreover, our framework permits skill discovery even from offline data, thereby reducing the need for excessive real-world interactions. We demonstrate empirically that LiSP successfully enables long-horizon planning and learns agents that can avoid catastrophic failures even in challenging non-stationary and non-episodic environments derived from gridworld and MuJoCo benchmarks 1 .arXiv:2012.03548v3 [cs.LG] 15 Jun 2021Published as a conference paper at ICLR 2021 This illustrative experiment complements prior work highlighting other failures of RL algorithms in non-stationary and non-episodic environments: Co-Reyes et al. (2020) find current RL algorithms fail to learn in a simple gridworld environment without resets and Lu et al. (2019) find modelfree RL algorithms struggle to learn and adapt to nonstationarity even with access to the ground truth dynamics model. We can attribute these failures to RL algorithms succumbing to sink states. Intuitively, these are states from which agents struggle to escape, have low rewards, and suggest a catastrophic halting of learning progress (Lyapunov, 1992). For example, an upright walking agent may fall over and fail to return to a standing position, possibly because of underactuated joints. A less obvious notion of sink state we use is that the agent simply fails to escape from it due to low learning signal, which is almost equally undesirable. A lifelong agent must seek to avoid such disabling sink states, especially in the absence of resets.We introduce Lifelong Skill Planning (LiSP), an algorithmic framework for reset-free, lifelong RL that uses long-horizon, decision-time planning in an abstract space of skills to overcome the above challenges. LiSP employs a synergistic combination of model-free policy networks and model-based planning, wherein we use a policy to execute certain skills, planning directly in the skill space. This combination offers two benefits: (1) skills constrain the search space to aid the planner in finding solutions to long-horizon problems and (2) skills mitigate errors in the dynamics model by constraining the distribution of behaviors. We demonstrate that agents learned via LiSP can effectively plan for longer horizons than prior work, enabling better long-term reasoning and adaptation.Another key component of the LiSP framework is the flexibility to learn skills from both online and offline interactions. For online learning, we extend Dynamics-Aware Discovery of Skills (DADS), an algorithm for unsupervised skill discovery(Sharma et al., 2019), with a skill-practice proposal distribution and a primitive dynamics model for generating rollouts for training. We demonstrate that the use of this proposal distribution significantly amplifies the signal for learning skills in resetfree settings. For offline learning from logged interactions, we employ a similar approach as above but with a modification of the reward function to correspond to the extent of disagreement amongst the models in a probabilistic ensemble (Kidambi et al., 2020).Our key contributions can be summarized as follows:• We identify skills as a key ingredient for overcoming the challenges to achieve effective lifelong RL in reset-free environments. algorithms. arXiv preprint arXiv. Model-based offline planning. arXiv preprint arXiv:2008.05556, 2020.Pierre-Luc Bacon, Jean Harb, and Doina Precup. The option-critic architecture. In Torres. Explore, discover and learn: Unsupervised discovery of state-covering skills. | RESET-FREE LIFELONG LEARNING WITH SKILL-SPACE PLANNING |
d257767456 | Conditional neural processes (CNPs; Garnelo et al., 2018a) are attractive metalearning models which produce well-calibrated predictions and are trainable via a simple maximum likelihood procedure. Although CNPs have many advantages, they are unable to model dependencies in their predictions. Various works propose solutions to this, but these come at the cost of either requiring approximations or being limited to Gaussian predictions. In this work, we instead propose to change how CNPs are deployed at test time, without any modifications to the model or training procedure. Instead of making predictions independently for every target point, we autoregressively define a joint predictive distribution using the chain rule of probability, taking inspiration from the neural autoregressive density estimator (NADE) literature. We show that this simple procedure allows factorised Gaussian CNPs to model highly dependent, non-Gaussian predictive distributions. Perhaps surprisingly, in an extensive range of tasks with synthetic and real data, we show that CNPs in autoregressive (AR) mode not only significantly outperform non-AR CNPs, but are also competitive with more sophisticated models that are significantly more expensive and challenging to train. This performance is remarkable since AR CNPs are not trained to model joint dependencies. Our work provides an example of how ideas from neural distribution estimation can benefit neural processes, motivating research into the AR deployment of other neural process models. | AUTOREGRESSIVE CONDITIONAL NEURAL PROCESSES |
d220302636 | Multimodal learning for generative models often refers to the learning of abstract concepts from the commonality of information in multiple modalities, such as vision and language. While it has proven effective for learning generalisable representations, the training of such models often requires a large amount of "related" multimodal data that shares commonality, which can be expensive to come by. To mitigate this, we develop a novel contrastive framework for generative model learning, allowing us to train the model not just by the commonality between modalities, but by the distinction between "related" and "unrelated" multimodal data. We show in experiments that our method enables data-efficient multimodal learning on challenging datasets for various multimodal variational autoencoder (VAE) models. We also show that under our proposed framework, the generative model can accurately identify related samples from unrelated ones, making it possible to make use of the plentiful unlabeled, unpaired multimodal data. | Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models |
d252716080 | We present a general framework for evolutionary learning to emergent unbiased state representation without any supervision. Evolutionary frameworks such as self-play converge to bad local optima in case of multi-agent reinforcement learning in non-cooperative partially observable environments with communication due to information asymmetry. Our proposed framework is a simple modification of selfplay inspired by mechanism design, also known as reverse game theory, to elicit truthful signals and make the agents cooperative. The key idea is to add imaginary rewards using the peer prediction method, i.e., a mechanism for evaluating the validity of information exchanged between agents in a decentralized environment. Numerical experiments with predator prey, traffic junction and StarCraft tasks demonstrate that the state-of-the-art performance of our framework. | TRUTHFUL SELF-PLAY |
d257279944 | A Capoeira practice. One is kicking and the other is avoiding the kick."Recent work has demonstrated the significant potential of denoising diffusion models for generating human motion, including text-to-motion capabilities. However, these methods are restricted by the paucity of annotated motion data, a focus on single-person motions, and a lack of detailed control. In this paper, we introduce three forms of composition based on diffusion priors: sequential, parallel, and model composition. Using sequential composition, we tackle the challenge of long sequence generation. We introduce DoubleTake, an inference-time method with which we generate long animations consisting of sequences of prompted intervals and their transitions, using a prior trained only for short clips. Using parallel composition, we show promising steps toward two-person generation. Beginning with two fixed priors as well as a few two-person training examples, we learn a slim communication block, ComMDM, to coordinate interaction between the two resulting motions. Lastly, using model composition, we first train individual priors to complete motions that realize a prescribed motion for a given joint. We then introduce DiffusionBlending, an interpolation mechanism to effectively blend several such models to enable flexible and efficient fine-grained joint and trajectory-level control and editing. We evaluate the composition methods using an off-the-shelf motion diffusion model, and further compare the results to dedicated models trained for these specific tasks. https://priormdm.github.io/priorMDM-page/ 1 | Human Motion Diffusion as a Generative Prior |
d237353080 | Deep neural network (DNN) model compression for efficient on-device inference becomes increasingly important to reduce memory requirements and keep user data on-device. To this end, we propose a novel differentiable k-means clustering layer (DKM) and its application to train-time weight-clustering for DNN model compression. DKM casts k-means clustering as an attention problem and enables joint optimization of the DNN parameters and clustering centroids. Unlike prior works that rely on additional parameters and regularizers, DKM-based compression keeps the original loss function and model architecture fixed. We evaluated DKM-based compression on various DNN models for computer vision and natural language processing (NLP) tasks. Our results demonstrate that DKM delivers superior compression and accuracy trade-off on ImageNet1k and GLUE benchmarks. For example, DKM-based compression can offer 74.5% top-1 Ima-geNet1k accuracy on ResNet50 with 3.3MB model size (29.4x model compression factor). For MobileNet-v1, which is a challenging DNN to compress, DKM delivers 63.9% top-1 ImageNet1k accuracy with 0.72 MB model size (22.4x model compression factor). This result is 6.8% higher top-1 accuracy and 33% relatively smaller model size than the current state-of-the-art DNN compression algorithms. DKM also compressed a DistilBERT model by 11.8x with minimal (1.1%) accuracy loss on GLUE NLP benchmarks. * equal contribution Published as a conference paper at ICLR 2022 | DKM: DIFFERENTIABLE k-MEANS CLUSTERING LAYER FOR NEURAL NETWORK COMPRESSION |
d204008396 | Learning multilingual representations of text has proven a successful method for many cross-lingual transfer learning tasks. There are two main paradigms for learning such representations: (1) alignment, which maps different independently trained monolingual representations into a shared space, and (2) joint training, which directly learns unified multilingual representations using monolingual and cross-lingual objectives jointly. In this paper, we first conduct direct comparisons of representations learned using both of these methods across diverse crosslingual tasks. Our empirical results reveal a set of pros and cons for both methods, and show that the relative performance of alignment versus joint training is taskdependent. Stemming from this analysis, we propose a simple and novel framework that combines these two previously mutually-exclusive approaches. Extensive experiments demonstrate that our proposed framework alleviates limitations of both approaches, and outperforms existing methods on the MUSE bilingual lexicon induction (BLI) benchmark. We further show that this framework can generalize to contextualized representations such as Multilingual BERT, and produces state-of-the-art results on the CoNLL cross-lingual NER benchmark. 1 Published as a conference paper at ICLR 2020The joint vocabulary is composed of three disjoint sets:is the shared vocabulary set and V i J is the set of tokens that appear in the ith language only. Note that a key difference of existing supervised joint training methods is that embeddings corresponding to V s J are not shared between E L1 and E L2 , meaning that they are disjoint, as in alignment methods.DISCUSSIONWhile alignment methods have had great success, there are still some critical downsides, among which we stress the following points: | CROSS-LINGUAL ALIGNMENT VS JOINT TRAINING: A COMPARATIVE STUDY AND A SIMPLE UNIFIED FRAMEWORK |
d219708931 | Recent studies have shown that skeletonization (pruning parameters) of networks at initialization provides all the practical benefits of sparsity both at inference and training time, while only marginally degrading their performance. However, we observe that beyond a certain level of sparsity (approx 95%), these approaches fail to preserve the network performance, and to our surprise, in many cases perform even worse than trivial random pruning. To this end, we propose to find a skeletonized network with maximum foresight connection sensitivity (FORCE). Intuitively, out of all possible sub-networks, we propose to find the one whose connections would have a maximum impact on the loss when perturbed. Our approximate solution to maximize the FORCE, progressively prunes connections of a given network at initialization. This allows parameters that were unimportant at earlier stages of skeletonization to become important at later stages. In many cases, our approach enables us to remove up to 99.9% parameters, while keeping networks trainable and providing significantly better performance than recent approaches. We demonstrate the effectiveness of our approach at various levels of sparsity (from medium to extreme) through extensive experiments and analysis. Code can be found in https://github.com/naver/force. | Progressive Skeletonization: Trimming more fat from a network at initialization |
d249191769 | While deep learning has outperformed other methods for various tasks, theoretical frameworks that explain its reason have not been fully established. To address this issue, we investigate the excess risk of two-layer ReLU neural networks in a teacher-student regression model, in which a student network learns an unknown teacher network through its outputs. Especially, we consider the student network that has the same width as the teacher network and is trained in two phases: first by noisy gradient descent and then by the vanilla gradient descent. Our result shows that the student network provably reaches a near-global optimal solution and outperforms any kernel methods estimator (more generally, linear estimators), including neural tangent kernel approach, random feature model, and other kernel methods, in a sense of the minimax optimal rate. The key concept inducing this superiority is the non-convexity of the neural network models. Even though the loss landscape is highly non-convex, the student network adaptively learns the teacher neurons. | Excess Risk of Two-Layer ReLU Neural Networks in Teacher-Student Settings and its Superiority to Kernel Methods |
d256598122 | Transfer learning is known to perform efficiently in many applications empirically, yet limited literature reports the mechanism behind the scene. This study establishes both formal derivations and heuristic analysis to formulate the theory of transfer learning in deep learning. Our framework utilizing layer variational analysis proves that the success of transfer learning can be guaranteed with corresponding data conditions. Moreover, our theoretical calculation yields intuitive interpretations towards the knowledge transfer process. Subsequently, an alternative method for network-based transfer learning is derived. The method shows an increase in efficiency and accuracy for domain adaptation. It is particularly advantageous when new domain data is sufficiently sparse during adaptation. Numerical experiments over diverse tasks validated our theory and verified that our analytic expression achieved better performance in domain adaptation than the gradient descent method. | INTERPRETATIONS OF DOMAIN ADAPTATIONS VIA LAYER VARIATIONAL ANALYSIS |
d203592088 | Understanding three-dimensional (3D) geometries from two-dimensional (2D) images without any labeled information is promising for understanding the real world without incurring annotation cost. We herein propose a novel generative model, RGBD-GAN, which achieves unsupervised 3D representation learning from 2D images. The proposed method enables camera parameter conditional image generation and depth image generation without any 3D annotations such as camera poses or depth. We used an explicit 3D consistency loss for two RGBD images generated from different camera parameters in addition to the ordinal GAN objective. The loss is simple yet effective for any type of image generator such as the DCGAN and StyleGAN to be conditioned on camera parameters. We conducted experiments and demonstrated that the proposed method could learn 3D representations from 2D images with various generator architectures.Recently, 3D representation learning through 3D object generation has been actively researched. Many techniques are available for learning the relationship between 2D images and 3D objects, which is called 3D object reconstruction from 2D images. Typically used 3D representations are voxel grids (Yan et al. | RGBD-GAN: UNSUPERVISED 3D REPRESENTATION LEARNING FROM NATURAL IMAGE DATASETS VIA RGBD IMAGE SYNTHESIS |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.