_id stringlengths 4 10 | text stringlengths 0 18.4k | title stringlengths 0 8.56k |
|---|---|---|
d238354231 | We introduce Noisy Feature Mixup (NFM), an inexpensive yet effective method for data augmentation that combines the best of interpolation based training and noise injection schemes. Rather than training with convex combinations of pairs of examples and their labels, we use noise-perturbed convex combinations of pairs of data points in both input and feature space. This method includes mixup and manifold mixup as special cases, but it has additional advantages, including better smoothing of decision boundaries and enabling improved model robustness. We provide theory to understand this as well as the implicit regularization effects of NFM. Our theory is supported by empirical results, demonstrating the advantage of NFM, as compared to mixup and manifold mixup. We show that residual networks and vision transformers trained with NFM have favorable trade-offs between predictive accuracy on clean data and robustness with respect to various types of data perturbation across a range of computer vision benchmark datasets. * equal contributions | Noisy Feature Mixup |
d226278174 | Neural link predictors are immensely useful for identifying missing edges in large scale Knowledge Graphs. However, it is still not clear how to use these models for answering more complex queries that arise in a number of domains, such as queries using logical conjunctions (∧), disjunctions (∨) and existential quantifiers (∃), while accounting for missing edges. In this work, we propose a framework for efficiently answering complex queries on incomplete Knowledge Graphs. We translate each query into an end-to-end differentiable objective, where the truth value of each atom is computed by a pre-trained neural link predictor. We then analyse two solutions to the optimisation problem, including gradient-based and combinatorial search. In our experiments, the proposed approach produces more accurate results than state-of-the-art methods -black-box neural models trained on millions of generated queries -without the need of training on a large and diverse set of complex queries. Using orders of magnitude less training data, we obtain relative improvements ranging from 8% up to 40% in Hits@3 across different knowledge graphs containing factual information. Finally, we demonstrate that it is possible to explain the outcome of our model in terms of the intermediate solutions identified for each of the complex query atoms.Neural link predictors(Nickel et al., 2016)tackle the problem of identifying missing edges in large KGs. However, in many complex domains, an open challenge is developing techniques for answering complex queries involving multiple and potentially unobserved edges, entities, and variables, rather than just single edges.We focus on First-Order Logical Queries that use conjunctions (∧), disjunctions (∨), and existential quantifiers (∃). A multitude of queries can be expressed by using such operators -for instance, the query "Which drugs D interact with proteins associated with diseases t 1 or t 2 ?" can be rewritten as ?D : ∃P.interacts(D, P ) ∧ [assoc(P, t 1 ) ∨ assoc(P, t 2 )], which can be answered via sub-graph matching.However, plain sub-graph matching cannot capture semantic similarities between entities and relations, and cannot deal with missing facts in the KG. One possible solution consists in computing † Equal contribution, alphabetical order. | COMPLEX QUERY ANSWERING WITH NEURAL LINK PREDICTORS |
d249240397 | Recent advances in the understanding of Generative Adversarial Networks (GANs) have led to remarkable progress in visual editing and synthesis tasks, capitalizing on the rich semantics that are embedded in the latent spaces of pre-trained GANs. However, existing methods are often tailored to specific GAN architectures and are limited to either discovering global semantic directions that do not facilitate localized control, or require some form of supervision through manually provided regions or segmentation masks. In this light, we present an architecture-agnostic approach that jointly discovers factors representing spatial parts and their appearances in an entirely unsupervised fashion. These factors are obtained by applying a semi-nonnegative tensor factorization on the feature maps, which in turn enables context-aware local image editing with pixel-level control. In addition, we show that the discovered appearance factors correspond to saliency maps that localize concepts of interest, without using any labels. Experiments on a wide range of GAN architectures and datasets show that, in comparison to the state of the art, our method is far more efficient in terms of training time and, most importantly, provides much more accurate localized control. Our code is available at https://github.com/james-oldfield/PandA. | PANDA: UNSUPERVISED LEARNING OF PARTS AND APPEARANCES IN THE FEATURE MAPS OF GANS |
d52895409 | Adversarial learning methods have been proposed for a wide range of applications, but the training of adversarial models can be notoriously unstable. Effectively balancing the performance of the generator and discriminator is critical, since a discriminator that achieves very high accuracy will produce relatively uninformative gradients. In this work, we propose a simple and general technique to constrain information flow in the discriminator by means of an information bottleneck. By enforcing a constraint on the mutual information between the observations and the discriminator's internal representation, we can effectively modulate the discriminator's accuracy and maintain useful and informative gradients. We demonstrate that our proposed variational discriminator bottleneck (VDB) leads to significant improvements across three distinct application areas for adversarial learning algorithms. Our primary evaluation studies the applicability of the VDB to imitation learning of dynamic continuous control skills, such as running. We show that our method can learn such skills directly from raw video demonstrations, substantially outperforming prior adversarial imitation learning methods. The VDB can also be combined with adversarial inverse reinforcement learning to learn parsimonious reward functions that can be transferred and re-optimized in new settings. Finally, we demonstrate that VDB can train GANs more effectively for image generation, improving upon a number of prior stabilization methods. (Video 1 ) | VARIATIONAL DISCRIMINATOR BOTTLENECK: IMPROVING IMITATION LEARNING, INVERSE RL, AND GANS BY CONSTRAINING INFORMATION FLOW |
d252992508 | We consider robust clustering problems in R d , specifically k-clustering problems (e.g., k-Median and k-Means) with m outliers, where the cost for a given center set C ⊂ R d aggregates the distances from C to all but the furthest m data points, instead of all points as in classical clustering. We focus on the -coreset for robust clustering, a small proxy of the dataset that preserves the clustering cost within -relative error for all center sets. Our main result is an -coreset of size O(m + poly(k −1 )) that can be constructed in near-linear time. This significantly improves previous results, which either suffers an exponential dependence on (m + k) [FS12], or has a weaker bi-criteria guarantee[HJLW18]. Furthermore, we show this dependence in m is nearly-optimal, and the fact that it is isolated from other factors may be crucial for dealing with large number of outliers. We construct our coresets by adapting to the outlier setting a recent framework [BCAJ + 22] which was designed for capacity-constrained clustering, overcoming a new challenge that the participating terms in the cost, particularly the excluded m outlier points, are dependent on the center set C. We validate our coresets on various datasets, and we observe a superior size-accuracy tradeoff compared with popular baselines including uniform sampling and sensitivity sampling. We also achieve a significant speedup of existing approximation algorithms for robust clustering using our coresets. * Here, L denotes the set of outliers, dist denotes the Euclidean distance, and dist(x, C) := min c∈C dist(x, c). Intuitively, the outliers capture the furthest points in a cluster which are "not well-clustered" and are most likely to be the noise. Notice that the parameter z captures various (robust) clustering problems, including (k, m)-Robust Median (where z = 1), (k, m)-Robust Means (where z = 2). On the other hand, if the number of outliers m = 0 then the robust clustering problem falls back to the non-robust version. The (k, z, m)-Robust Clustering problem has been widely studied in the literature [Che08, GKL + 17, KLS18, FKRS19, SRF20]. Moreover, the idea of removing outliers has been also considered in other machine learning tasks, e.g., robust PCA [BK18] and robust regression [RL87, MNP + 14].Computational Challenges However, the presence of outliers introduces significant computational challenges, and it inspires a series of research to design efficient algorithms for robust clustering. On one hand, approximation algorithms with strict accuracy guarantee has been obtained [CKMN01, Che08, GKL + 17, KLS18, FZH + 19, FKRS19, ZFH + 21] but their running time is a high-degree polynomial which is impractical.On the other hand, more scalable algorithms were also proposed [BVX19, DKP20], however, the approximation ratio is worse, and a more severe limitation is that their guarantee usually violates the required number of outliers. Moreover, to the best of our knowledge, we are not aware of works that design algorithms in sublinear models, such as streaming and distributed computing.Coresets In order to tackle the computational challenges, we consider coresets for robust clustering.Roughly, an -coreset is a tiny proxy of the massive input dataset, on which the clustering objective is preserved within -error for every potential center set. Existing algorithms may benefit a significant speedup if running on top of a coreset, and more importantly, coresets can be used to derive sublinear algorithms, including streaming algorithms [HM04], distributed algorithms [BEL13] and dynamic algorithms [HK20], which are highly useful to deal with massive datasets. Stemming from [HM04], the study of coresets for the non-robust version of clustering, i.e., (k, z)-Clustering, has been very fruitful [FL11, FSS20, SW18, HV20, BJKW21, CSS21b, BCAJ + 22], and the state-of-the-art coreset achieves a size poly(k −1 ), independent of d and n. However, coresets for robust clustering were much less understood. Existing results either suffers an exponential (k + m) k+m factor in the coreset size [FS12], or needs to violate the required number of outliers[HJLW18]. This gap leads to the following question: can we efficiently construct an -coreset of size poly(m, k, −1 ) for (k, z, m)-Robust Clustering (without violating the number of outliers)? | Near-optimal Coresets for Robust Clustering |
d10756563 | We study the problem of transferring a sample in one domain to an analog sample in another domain. Given two related domains, S and T , we would like to learn a generative function G that maps an input sample from S to the domain T , such that the output of a given function f , which accepts inputs in either domains, would remain unchanged. Other than the function f , the training data is unsupervised and consist of a set of samples from each domain. The Domain Transfer Network (DTN) we present employs a compound loss function that includes a multiclass GAN loss, an f -constancy component, and a regularizing component that encourages G to map samples from T to themselves. We apply our method to visual domains including digits and face images and demonstrate its ability to generate convincing novel images of previously unseen entities, while preserving their identity. | UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION |
d260551862 | Adversarial training provides a means of regularizing supervised learning algorithms while virtual adversarial training is able to extend supervised learning algorithms to the semi-supervised setting. However, both methods require making small perturbations to numerous entries of the input vector, which is inappropriate for sparse high-dimensional inputs such as one-hot word representations. We extend adversarial and virtual adversarial training to the text domain by applying perturbations to the word embeddings in a recurrent neural network rather than to the original input itself. The proposed method achieves state of the art results on multiple benchmark semi-supervised and purely supervised tasks. We provide visualizations and analysis showing that the learned word embeddings have improved in quality and that while training, the model is less prone to overfitting. Code is available at | ADVERSARIAL TRAINING METHODS FOR SEMI-SUPERVISED TEXT CLASSIFICATION |
d208139512 | Outlier detection and novelty detection are two important topics for anomaly detection. Suppose the majority of a dataset are drawn from a certain distribution, outlier detection and novelty detection both aim to detect data samples that do not fit the distribution. Outliers refer to data samples within this dataset, while novelties refer to new samples. In the meantime, backdoor poisoning attacks for machine learning models are achieved through injecting poisoning samples into the training dataset, which could be regarded as "outliers" that are intentionally added by attackers. Differential privacy has been proposed to avoid leaking any individual's information, when aggregated analysis is performed on a given dataset. It is typically achieved by adding random noise, either directly to the input dataset, or to intermediate results of the aggregation mechanism. In this paper, we demonstrate that applying differential privacy can improve the utility of outlier detection and novelty detection, with an extension to detect poisoning samples in backdoor attacks. We first present a theoretical analysis on how differential privacy helps with the detection, and then conduct extensive experiments to validate the effectiveness of differential privacy in improving outlier detection, novelty detection, and backdoor attack detection. | ROBUST ANOMALY DETECTION AND BACKDOOR ATTACK DETECTION VIA DIFFERENTIAL PRIVACY |
d262217283 | Recurrent neural networks (RNNs) in the brain and in silico excel at solving tasks with intricate temporal dependencies.Long timescales required for solving such tasks can arise from properties of individual neurons (single-neuron timescale, τ , e.g., membrane time constant in biological neurons) or recurrent interactions among them (network-mediated timescale, τ net ).However, the contribution of each mechanism for optimally solving memory-dependent tasks remains poorly understood.Here, we train RNNs to solve N -parity and N -delayed match-to-sample tasks with increasing memory requirements controlled by N by simultaneously optimizing recurrent weights and τ s.We find that for both tasks RNNs develop longer timescales with increasing N , but depending on the learning objective, they use different mechanisms.Two distinct curricula define learning objectives: sequential learning of a single-N (single-head) or simultaneous learning of multiple N s (multi-head).Single-head networks increase their τ with N and are able to solve tasks for large N , but they suffer from catastrophic forgetting.However, multi-head networks, which are explicitly required to hold multiple concurrent memories, keep τ constant and develop longer timescales through recurrent connectivity.Moreover, we show that the multi-head curriculum increases training speed and network stability to ablations and perturbations, and allows RNNs to generalize better to tasks beyond their training regime.This curriculum also significantly improves training GRUs and LSTMs for large-N tasks.Our results suggest that adapting timescales to task requirements via recurrent interactions allows learning more complex objectives and improves the RNN's performance. | Emergent mechanisms for long timescales depend on training curriculum and affect performance in memory tasks |
d235417295 | Generalization is one of the fundamental issues in machine learning. However, traditional techniques like uniform convergence may be unable to explain generalization under overparameterization (Nagarajan & Kolter, 2019). As alternative approaches, techniques based on stability analyze the training dynamics and derive algorithm-dependent generalization bounds. Unfortunately, the stability-based bounds are still far from explaining the surprising generalization in deep learning since neural networks usually suffer from unsatisfactory stability. This paper proposes a novel decomposition framework to improve the stability-based bounds via a more fine-grained analysis of the signal and noise, inspired by the observation that neural networks converge relatively slowly when fitting noise (which indicates better stability). Concretely, we decompose the excess risk dynamics and apply the stability-based bound only on the noise component. The decomposition framework performs well in both linear regimes (overparameterized linear regression) and non-linear regimes (diagonal matrix recovery). Experiments on neural networks verify the utility of the decomposition framework. * Equal contribution. of stochastic gradient descent on nonsmooth convex losses. arXiv preprint arXiv:2006.06914, 2020.Olivier Bousquet and André Elisseeff. Stability and generalization. | TOWARDS UNDERSTANDING GENERALIZATION VIA DECOMPOSING EXCESS RISK DYNAMICS |
d5834589 | The stochastic gradient descent (SGD) method and its variants are algorithms of choice for many Deep Learning tasks. These methods operate in a small-batch regime wherein a fraction of the training data, say 32-512 data points, is sampled to compute an approximation to the gradient. It has been observed in practice that when using a larger batch there is a degradation in the quality of the model, as measured by its ability to generalize. We investigate the cause for this generalization drop in the large-batch regime and present numerical evidence that supports the view that large-batch methods tend to converge to sharp minimizers of the training and testing functions-and as is well known, sharp minima lead to poorer generalization. In contrast, small-batch methods consistently converge to flat minimizers, and our experiments support a commonly held view that this is due to the inherent noise in the gradient estimation. We discuss several strategies to attempt to help large-batch methods eliminate this generalization gap. Published as a conference paper at ICLR 2017 These methods minimize the objective function f by iteratively taking steps of the form: J. Duchi, E. Hazan, and Y. Singer. Adaptive subgradient methods for online learning and stochastic optimization. . Weak sharp minima and penalty functions in mathematical programming. PhD thesis, University of Cambridge, 1988.Michael P Friedlander and Mark Schmidt. Hybrid deterministic-stochastic methods for data fitting. | ON LARGE-BATCH TRAINING FOR DEEP LEARNING: GENERALIZATION GAP AND SHARP MINIMA |
d235613642 | Point cloud sequences are irregular and unordered in the spatial dimension while exhibiting regularities and order in the temporal dimension. Therefore, existing grid based convolutions for conventional video processing cannot be directly applied to spatio-temporal modeling of raw point cloud sequences. In this paper, we propose a point spatio-temporal (PST) convolution to achieve informative representations of point cloud sequences. The proposed PST convolution first disentangles space and time in point cloud sequences. Then, a spatial convolution is employed to capture the local structure of points in the 3D space, and a temporal convolution is used to model the dynamics of the spatial regions along the time dimension. Furthermore, we incorporate the proposed PST convolution into a deep network, namely PSTNet, to extract features of point cloud sequences in a hierarchical manner. Extensive experiments on widely-used 3D action recognition and 4D semantic segmentation datasets demonstrate the effectiveness of PSTNet to model point cloud sequences. . Prototype propagation networks (PPN) for weakly-supervised few-shot learning on category graph. In IJCAI, 2019c. | PSTNET: POINT SPATIO-TEMPORAL CONVOLUTION ON POINT CLOUD SEQUENCES |
d257365136 | Prompt tuning, in which a base pretrained model is adapted to each task via conditioning on learned prompt vectors, has emerged as a promising approach for efficiently adapting large language models to multiple downstream tasks. However, existing methods typically learn soft prompt vectors from scratch, and it has not been clear how to exploit the rich cross-task knowledge with prompt vectors in a multitask learning setting. We propose multitask prompt tuning (MPT), which first learns a single transferable prompt by distilling knowledge from multiple task-specific source prompts. We then learn multiplicative low rank updates to this shared prompt to efficiently adapt it to each downstream target task. Extensive experiments on 23 NLP datasets demonstrate that our proposed approach outperforms the state-of-the-art methods, including the full finetuning baseline in some cases, despite only tuning 0.035% as many task-specific parameters. 1 * Work done during an internship at MIT-IBM Watson AI Lab. | MULTITASK PROMPT TUNING ENABLES PARAMETER-EFFICIENT TRANSFER LEARNING |
d257427516 | Algorithmic case-based decision support provides examples to aid people in decision making tasks by providing contexts for a test case. Despite the promising performance of supervised learning, representations learned by supervised models may not align well with human intuitions: what models consider similar examples can be perceived as distinct by humans. As a result, they have limited effectiveness in case-based decision support. In this work, we incorporate ideas from metric learning with supervised learning to examine the importance of alignment for effective decision support. In addition to instance-level labels, we use human-provided triplet judgments to learn human-compatible decision-focused representations. Using both synthetic data and human subject experiments in multiple classification tasks, we demonstrate that such representation is better aligned with human perception than representation solely optimized for classification. Human-compatible representations identify nearest neighbors that are perceived as more similar by humans and allow humans to make more accurate predictions, leading to substantial improvements in human decision accuracies (17.8% in butterfly vs. moth classification and 13.2% in pneumonia classification). | LEARNING HUMAN-COMPATIBLE REPRESENTATIONS FOR CASE-BASED DECISION SUPPORT |
d3532296 | Successful recurrent models such as long short-term memories (LSTMs) and gated recurrent units (GRUs) use ad hoc gating mechanisms. Empirically these models have been found to improve the learning of medium to long term temporal dependencies and to help with vanishing gradient issues. We prove that learnable gates in a recurrent model formally provide quasiinvariance to general time transformations in the input data. We recover part of the LSTM architecture from a simple axiomatic approach. This result leads to a new way of initializing gate biases in LSTMs and GRUs. Experimentally, this new chrono initialization is shown to greatly improve learning of long term dependencies, with minimal implementation effort.Recurrent neural networks (e.g.(Jaeger, 2002)) are a standard machine learning tool to model and represent temporal data; mathematically they amount to learning the parameters of a parameterized dynamical system so that its behavior optimizes some criterion, such as the prediction of the next data in a sequence.Published as a conference paper at ICLR 2018 explains why this is reasonable in most cases, when facing medium term dependencies, but fails when facing long to very long term dependencies. | CAN RECURRENT NEURAL NETWORKS WARP TIME? |
d174801410 | Traditional set prediction models can struggle with simple datasets due to an issue we call the responsibility problem. We introduce a pooling method for sets of feature vectors based on sorting features across elements of the set. This can be used to construct a permutation-equivariant auto-encoder that avoids this responsibility problem. On a toy dataset of polygons and a set version of MNIST, we show that such an auto-encoder produces considerably better reconstructions and representations. Replacing the pooling function in existing set encoders with FSPool improves accuracy and convergence speed on a variety of datasets. | FSPOOL: LEARNING SET REPRESENTATIONS WITH FEATUREWISE SORT POOLING |
d201666520 | In this paper we consider self-supervised representation learning to improve sample efficiency in reinforcement learning (RL). We propose a forward prediction objective for simultaneously learning embeddings of states and action sequences. These embeddings capture the structure of the environment's dynamics, enabling efficient policy learning. We demonstrate that our action embeddings alone improve the sample efficiency and peak performance of model-free RL on control from low-dimensional states. By combining state and action embeddings, we achieve efficient learning of high-quality policies on goal-conditioned continuous control from pixel observations in only 1-2 million environment steps. | DYNAMICS-AWARE EMBEDDINGS |
d52908393 | Deep neuroevolution and deep reinforcement learning (deep RL) algorithms are two popular approaches to policy search. The former is widely applicable and rather stable, but suffers from low sample efficiency. By contrast, the latter is more sample efficient, but the most sample efficient variants are also rather unstable and highly sensitive to hyper-parameter setting. So far, these families of methods have mostly been compared as competing tools. However, an emerging approach consists in combining them so as to get the best of both worlds. Two previously existing combinations use either an ad hoc evolutionary algorithm or a goal exploration process together with the Deep Deterministic Policy Gradient (ddpg) algorithm, a sample efficient off-policy deep RL algorithm. In this paper, we propose a different combination scheme using the simple cross-entropy method (cem) and Twin Delayed Deep Deterministic policy gradient (td3), another off-policy deep RL algorithm which improves over ddpg. We evaluate the resulting method, cem-rl, on a set of benchmarks classically used in deep RL. We show that cem-rl benefits from several advantages over its competitors and offers a satisfactory trade-off between performance and sample efficiency. | CEM-RL: Combining evolutionary and gradient-based methods for policy search |
d199577786 | Natural question generation (QG) is a challenging yet rewarding task, that aims to generate questions given an input passage and a target answer. Previous works on QG, however, either (i) ignore the rich structure information hidden in the word sequence, (ii) fail to fully exploit the target answer, or (iii) solely rely on cross-entropy loss that leads to issues like exposure bias and evaluation discrepancy between training and testing. To address the above limitations, in this paper, we propose a reinforcement learning (RL) based graphto-sequence (Graph2Seq) architecture for the QG task. Our model consists of a Graph2Seq generator where a novel bidirectional graph neural network (GNN) based encoder is applied to embed the input passage incorporating the answer information via a simple yet effective Deep Alignment Network, and an evaluator where a mixed objective function combining both cross-entropy loss and RL loss is designed for ensuring the generation of semantically and syntactically valid text. The proposed model is end-to-end trainable, and achieves new state-of-the-art scores and outperforms all previous methods by a great margin on the SQuAD benchmark. | Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation |
d218595798 | In this paper we propose Flowtron: an autoregressive flow-based generative network for textto-speech synthesis with control over speech variation and style transfer. Flowtron borrows insights from IAF and revamps Tacotron in order to provide high-quality and expressive melspectrogram synthesis. Flowtron is optimized by maximizing the likelihood of the training data, which makes training simple and stable. Flowtron learns an invertible mapping of data to a latent space that can be manipulated to control many aspects of speech synthesis (pitch, tone, speech rate, cadence, accent). Our mean opinion scores (MOS) show that Flowtron matches state-of-the-art TTS models in terms of speech quality. In addition, we provide results on control of speech variation, interpolation between samples and style transfer between speakers seen and unseen during training. Code and pretrained models will be made publicly available at https://github.com/NVIDIA/flowtron. | Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis |
d235313572 | Vision Transformers (ViTs) and MLPs signal further efforts on replacing handwired features or inductive biases with general-purpose neural architectures. Existing works empower the models by massive data, such as large-scale pre-training and/or repeated strong data augmentations, and still report optimization-related problems (e.g., sensitivity to initialization and learning rates). Hence, this paper investigates ViTs and MLP-Mixers from the lens of loss geometry, intending to improve the models' data efficiency at training and generalization at inference. Visualization and Hessian reveal extremely sharp local minima of converged models. By promoting smoothness with a recently proposed sharpnessaware optimizer, we substantially improve the accuracy and robustness of ViTs and MLP-Mixers on various tasks spanning supervised, adversarial, contrastive, and transfer learning (e.g., +5.3% and +11.0% top-1 accuracy on ImageNet for ViT-B/16 and Mixer-B/16, respectively, with the simple Inception-style preprocessing). We show that the improved smoothness attributes to sparser active neurons in the first few layers. The resultant ViTs outperform ResNets of similar size and throughput when trained from scratch on ImageNet without large-scale pre-training or strong data augmentations. Model checkpoints are available at https://github.com/google-research/vision_transformer. * Work done as a student researcher at Google. | WHEN VISION TRANSFORMERS OUTPERFORM RESNETS WITHOUT PRE-TRAINING OR STRONG DATA AUGMENTATIONS |
d251647798 | Learning a single static convolutional kernel 1 in each convolutional layer is the common training paradigm of modern Convolutional Neural Networks (CNNs). Instead, recent research in dynamic convolution shows that learning a linear combination of n convolutional kernels weighted with their input-dependent attentions can significantly improve the accuracy of light-weight CNNs, while maintaining efficient inference. However, we observe that existing works endow convolutional kernels with the dynamic property through one dimension (regarding the convolutional kernel number) of the kernel space, but the other three dimensions (regarding the spatial size, the input channel number and the output channel number for each convolutional kernel) are overlooked. Inspired by this, we present Omni-dimensional Dynamic Convolution (ODConv), a more generalized yet elegant dynamic convolution design, to advance this line of research. ODConv leverages a novel multi-dimensional attention mechanism with a parallel strategy to learn complementary attentions for convolutional kernels along all four dimensions of the kernel space at any convolutional layer. As a drop-in replacement of regular convolutions, ODConv can be plugged into many CNN architectures. Extensive experiments on the ImageNet and MS-COCO datasets show that OD-Conv brings solid accuracy boosts for various prevailing CNN backbones including both light-weight and large ones, e.g., 3.77%∼5.71%|1.86%∼3.72% absolute top-1 improvements to MobivleNetV2|ResNet family on the ImageNet dataset. Intriguingly, thanks to its improved feature learning ability, ODConv with even one single kernel can compete with or outperform existing dynamic convolution counterparts with multiple kernels, substantially reducing extra parameters. Furthermore, ODConv is also superior to other attention modules for modulating the output features or the convolutional weights. Code and models are available at https://github.com/OSVAI/ODConv. | OMNI-DIMENSIONAL DYNAMIC CONVOLUTION |
d52889459 | Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple "truncation trick," allowing fine control over the trade-off between sample fidelity and variety by truncating the latent space. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128×128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.3 and Fréchet Inception Distance (FID) of 9.6, improving over the previous best IS of 52.52 and FID of 18.65. | LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS |
d238419164 | What is the state of the art in continual machine learning? Although a natural question for predominant static benchmarks, the notion to train systems in a lifelong manner entails a plethora of additional challenges with respect to set-up and evaluation. The latter have recently sparked a growing amount of critiques on prominent algorithm-centric perspectives and evaluation protocols being too narrow, resulting in several attempts at constructing guidelines in favor of specific desiderata or arguing against the validity of prevalent assumptions. In this work, we depart from this mindset and argue that the goal of a precise formulation of desiderata is an ill-posed one, as diverse applications may always warrant distinct scenarios. Instead, we introduce the Continual Learning EValuation Assessment Compass: the CLEVA-Compass. The compass provides the visual means to both identify how approaches are practically reported and how works can simultaneously be contextualized in the broader literature landscape. In addition to promoting compact specification in the spirit of recent replication trends, it thus provides an intuitive chart to understand the priorities of individual systems, where they resemble each other, and what elements are missing towards a fair comparison. | CLEVA-COMPASS: A CONTINUAL LEARNING EVALUATION ASSESSMENT COMPASS TO PROMOTE RESEARCH TRANSPARENCY AND COMPARABILITY |
d264935143 | While Large Language Models (LLMs) are increasingly being used in real-world applications, they remain vulnerable to prompt injection attacks: malicious third party prompts that subvert the intent of the system designer.To help researchers study this problem, we present a dataset of over 126,000 prompt injection attacks and 46,000 prompt-based "defenses" against prompt injection, all created by players of an online game called Tensor Trust.To the best of our knowledge, this is currently the largest dataset of human-generated adversarial examples for instruction-following LLMs.The attacks in our dataset have a lot of easily interpretable stucture, and shed light on the weaknesses of LLMs.We also use the dataset to create a benchmark for resistance to two types of prompt injection, which we refer to as prompt extraction and prompt hijacking.Our benchmark results show that many models are vulnerable to the attack strategies in the Tensor Trust dataset.Furthermore, we show that some attack strategies from the dataset generalize to deployed LLM-based applications, even though they have a very different set of constraints to the game.We release all data and source code at tensortrust.ai/paper | TENSOR TRUST: INTERPRETABLE PROMPT INJECTION ATTACKS FROM AN ONLINE GAME |
d221136343 | Graph neural networks (GNNs) have demonstrated strong performance on a wide variety of tasks due to their ability to model non-uniform structured data. Despite their promise, there exists little research exploring methods to make these architectures more efficient at inference time. In this work, we explore the viability of training quantized GNNs models, enabling the usage of low precision integer arithmetic during inference. We identify the sources of error that uniquely arise when attempting to quantize GNNs, and propose a method, Degree-Quant, to improve performance over existing quantization-aware training baselines commonly used on other architectures, such as CNNs. Models trained with Degree-Quant for INT8 quantization perform as well as FP32 models in most cases; for INT4 models, we obtain up to 69% gains over the baselines. Our work provides a comprehensive set of experiments across several datasets for node classification, graph classification and graph regression, laying strong foundations for future work in this area. * Equal contribution Preprint. Under review. | Degree-Quant: Quantization-Aware Training for Graph Neural Networks |
d249152020 | Learning high-dimensional distributions is often done with explicit likelihood modeling or implicit modeling via minimizing integral probability metrics (IPMs). In this paper, we expand this learning paradigm to stochastic orders, namely, the convex or Choquet order between probability measures. Towards this end, exploiting the relation between convex orders and optimal transport, we introduce the Choquet-Toland distance between probability measures, that can be used as a drop-in replacement for IPMs. We also introduce the Variational Dominance Criterion (VDC) to learn probability measures with dominance constraints, that encode the desired stochastic order between the learned measure and a known baseline. We analyze both quantities and show that they suffer from the curse of dimensionality and propose surrogates via input convex maxout networks (ICMNs), that enjoy parametric rates. We provide a min-max framework for learning with stochastic orders and validate it experimentally on synthetic and high-dimensional image generation, with promising results. Finally, our ICMNs class of convex functions and its derived Rademacher Complexity are of independent interest beyond their application in convex orders. arXiv:2205.13684v2 [stat.ML] 9 Nov 2022 E. M. Bronshtein. -entropy of convex sets and functions. | Learning with Stochastic Orders |
d235390687 | In this work, we study the problem of clustering survival data -a challenging and so far under-explored task. We introduce a novel semi-supervised probabilistic approach to cluster survival data by leveraging recent advances in stochastic gradient variational inference. In contrast to previous work, our proposed method employs a deep generative model to uncover the underlying distribution of both the explanatory variables and censored survival times. We compare our model to the related work on clustering and mixture models for survival data in comprehensive experiments on a wide range of synthetic, semi-synthetic, and real-world datasets, including medical imaging data. Our method performs better at identifying clusters and is competitive at predicting survival times. Relying on novel generative assumptions, the proposed model offers a holistic perspective on clustering survival data and holds a promise of discovering subpopulations whose survival is regulated by different generative mechanisms. | A DEEP VARIATIONAL APPROACH TO CLUSTERING SURVIVAL DATA |
d252438904 | We provide theoretical convergence guarantees for score-based generative models (SGMs) such as denoising diffusion probabilistic models (DDPMs), which constitute the backbone of large-scale realworld generative models such as DALL·E 2. Our main result is that, assuming accurate score estimates, such SGMs can efficiently sample from essentially any realistic data distribution. In contrast to prior works, our results (1) hold for an L 2 -accurate score estimate (rather than L ∞ -accurate); (2) do not require restrictive functional inequality conditions that preclude substantial non-log-concavity; (3) scale polynomially in all relevant problem parameters; and (4) match state-of-the-art complexity guarantees for discretization of the Langevin diffusion, provided that the score error is sufficiently small. We view this as strong theoretical justification for the empirical success of SGMs. We also examine SGMs based on the critically damped Langevin diffusion (CLD). Contrary to conventional wisdom, we provide evidence that the use of the CLD does not reduce the complexity of SGMs.Providing an analysis which goes beyond these limitations is a pressing first step towards theoretically understanding why SGMs actually work in practice.Concurrent work. The concurrent and independent work of [LLT23] also obtains similar guarantees to our Corollary 3.Our contributionsIn this work, we take a step towards bridging theory and practice by providing a convergence guarantee for SGMs, under realistic (in fact, quite minimal) assumptions, which scales polynomially in all relevant problem parameters. Namely, our main result (Theorem 2) only requires the following assumptions on the data distribution q, which we make more quantitative in Section 3:A1 The score function of the forward process is L-Lipschitz.A2 The second moment of q is finite.A3 The data distribution q has finite KL divergence w.r.t. the standard Gaussian.We note that all of these assumptions are either standard or, in the case of A2, far weaker than what is needed in prior work. Crucially, unlike prior works, we do not assume log-concavity, an LSI, or dissipativity; hence, our assumptions cover arbitrarily non-log-concave data distributions. Our main result is summarized informally as follows.Theorem 1 (informal, see Theorem 2). Under assumptions A1-A3, and if the score estimation error in L 2 is at most O(ε), then with an appropriate choice of step size, the SGM outputs a measure which is ε-close in total variation (TV) distance to q in O(L 2 d/ε 2 ) iterations.We remark that our iteration complexity is actually quite tight: in fact, this matches state-of-the-art discretization guarantees for the Langevin diffusion [VW19; Che+21a].We find Theorem 1 to be quite surprising, because it shows that SGMs can sample from the data distribution q with polynomial complexity, even when q is highly non-log-concave (a task that is usually intractable), provided that one has access to an accurate score estimator. This answers the open question of [LLT22] regarding whether or not SGMs can sample from multimodal distributions, e.g., mixtures of distributions with bounded log-Sobolev constant. In the context of neural networks, our result implies that | Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions |
d29155623 | Network Embeddings (NEs) map the nodes of a given network into d-dimensional Euclidean space R d . Ideally, this mapping is such that 'similar' nodes are mapped onto nearby points, such that the NE can be used for purposes such as link prediction (if 'similar' means being 'more likely to be connected') or classification (if 'similar' means 'being more likely to have the same label'). In recent years various methods for NE have been introduced, all following a similar strategy: defining a notion of similarity between nodes (typically some distance measure within the network), a distance measure in the embedding space, and a loss function that penalizes large distances for similar nodes and small distances for dissimilar nodes. A difficulty faced by existing methods is that certain networks are fundamentally hard to embed due to their structural properties: (approximate) multipartiteness, certain degree distributions, assortativity, etc. To overcome this, we introduce a conceptual innovation to the NE literature and propose to create Conditional Network Embeddings (CNEs); embeddings that maximally add information with respect to given structural properties (e.g. node degrees, block densities, etc.). We use a simple Bayesian approach to achieve this, and propose a block stochastic gradient descent algorithm for fitting it efficiently. We demonstrate that CNEs are superior for link prediction and multi-label classification when compared to state-ofthe-art methods, and this without adding significant mathematical or computational complexity. Finally, we illustrate the potential of CNE for network visualization. | CONDITIONAL NETWORK EMBEDDINGS |
d49672236 | We study the problem of attacking a machine learning model in the hard-label black-box setting, where no model information is revealed except that the attacker can make queries to probe the corresponding hard-label decisions. This is a very challenging problem since the direct extension of state-of-the-art white-box attacks (e.g., C&W or PGD) to the hard-label black-box setting will require minimizing a non-continuous step function, which is combinatorial and cannot be solved by a gradient-based optimizer. The only current approach is based on random walk on the boundary [1], which requires lots of queries and lacks convergence guarantees. We propose a novel way to formulate the hard-label black-box attack as a real-valued optimization problem which is usually continuous and can be solved by any zeroth order optimization algorithm. For example, using the Randomized Gradient-Free method [2], we are able to bound the number of iterations needed for our algorithm to achieve stationary points. We demonstrate that our proposed method outperforms the previous random walk approach to attacking convolutional neural networks on MNIST, CIFAR, and ImageNet datasets. More interestingly, we show that the proposed algorithm can also be used to attack other discrete and non-continuous machine learning models, such as Gradient Boosting Decision Trees (GBDT). | Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach |
d247595275 | Preference-based reinforcement learning (RL) has shown potential for teaching agents to perform the target tasks without a costly, pre-defined reward function by learning the reward with a supervisor's preference between the two agent behaviors. However, preference-based learning often requires a large amount of human feedback, making it difficult to apply this approach to various applications. This data-efficiency problem, on the other hand, has been typically addressed by using unlabeled samples or data augmentation techniques in the context of supervised learning. Motivated by the recent success of these approaches, we present SURF, a semi-supervised reward learning framework that utilizes a large amount of unlabeled samples with data augmentation. In order to leverage unlabeled samples for reward learning, we infer pseudo-labels of the unlabeled samples based on the confidence of the preference predictor. To further improve the label-efficiency of reward learning, we introduce a new data augmentation that temporally crops consecutive subsequences from the original behaviors. Our experiments demonstrate that our approach significantly improves the feedback-efficiency of the state-ofthe-art preference-based method on a variety of locomotion and robotic manipulation tasks. | SURF: SEMI-SUPERVISED REWARD LEARNING WITH DATA AUGMENTATION FOR FEEDBACK-EFFICIENT PREFERENCE-BASED REINFORCEMENT LEARNING |
d215786510 | Many recent state-of-the-art methods for neural architecture search (NAS) relax the NAS problem into a joint continuous optimization over architecture parameters and their shared-weights, enabling the application of standard gradient-based optimizers. However, this training process remains poorly understood, as evidenced by the multitude of gradient-based heuristics that have been recently proposed. Invoking the theory of mirror descent, we present a unifying framework for designing and analyzing gradient-based NAS methods that exploit the underlying problem structure to quickly find high-performance architectures. Our geometry-aware framework leads to simple yet novel algorithms that (1) enjoy faster convergence guarantees than existing gradient-based methods and (2) achieve state-of-the-art accuracy on the latest NAS benchmarks in computer vision. Notably, we exceed the best published results for both CIFAR and ImageNet on both the DARTS search space and NAS-Bench-201; on the latter benchmark we achieve close to oracleoptimal performance on CIFAR-10 and CIFAR-100. Together, our theory and experiments demonstrate a principled way to co-design optimizers and continuous parameterizations of discrete NAS search spaces. * Denotes equal contribution. | Geometry-Aware Gradient Algorithms for Neural Architecture Search |
d235485294 | In shuffle privacy, each user sends a collection of randomized messages to a trusted shuffler, the shuffler randomly permutes these messages, and the resulting shuffled collection of messages must satisfy differential privacy. Prior work in this model has largely focused on protocols that use a single round of communication to compute algorithmic primitives like means, histograms, and counts. We present interactive shuffle protocols for stochastic convex optimization. Our protocols rely on a new noninteractive protocol for summing vectors of bounded ℓ 2 norm. By combining this sum subroutine with mini-batch stochastic gradient descent, accelerated gradient descent, and Nesterov's smoothing method, we obtain loss guarantees for a variety of convex loss functions that significantly improve on those of the local model and sometimes match those of the central model. | SHUFFLE PRIVATE STOCHASTIC CONVEX OPTIMIZATION |
d263831292 | Learning representations that generalize to novel compositions of known concepts is crucial for bridging the gap between human and machine perception.One prominent effort is learning object-centric representations, which are widely conjectured to enable compositional generalization.Yet, it remains unclear when this conjecture will be true, as a principled theoretical or empirical understanding of compositional generalization is lacking.In this work, we investigate when compositional generalization is guaranteed for object-centric representations through the lens of identifiability theory.We show that autoencoders that satisfy structural assumptions on the decoder and enforce encoder-decoder consistency will learn object-centric representations that provably generalize compositionally.We validate our theoretical result and highlight the practical relevance of our assumptions through experiments on synthetic image data. | PROVABLE COMPOSITIONAL GENERALIZATION FOR OBJECT-CENTRIC LEARNING |
d246823979 | From the intuitive notion of disentanglement, the image variations corresponding to different factors should be distinct from each other, and the disentangled representation should reflect those variations with separate dimensions. To discover the factors and learn disentangled representation, previous methods typically leverage an extra regularization term when learning to generate realistic images. However, the term usually results in a trade-off between disentanglement and generation quality. For the generative models pretrained without any disentanglement term, the generated images show semantically meaningful variations when traversing along different directions in the latent space. Based on this observation, we argue that it is possible to mitigate the trade-off by (i) leveraging the pretrained generative models with high generation quality, (ii) focusing on discovering the traversal directions as factors for disentangled representation learning. To achieve this, we propose Disentaglement via Contrast (DisCo) as a framework to model the variations based on the target disentangled representations, and contrast the variations to jointly discover disentangled directions and learn disentangled representations. DisCo achieves the state-of-the-art disentangled representation learning and distinct direction discovering, given pretrained nondisentangled generative models including GAN, VAE, and Flow. Source code is at https://github.com/xrenaa/DisCo. | LEARNING DISENTANGLED REPRESENTATION BY EXPLOITING PRETRAINED GENERATIVE MODELS: A CONTRASTIVE LEARNING VIEW |
d238531782 | Goal-conditioned hierarchical reinforcement learning (GCHRL) provides a promising approach to solving long-horizon tasks. Recently, its success has been extended to more general settings by concurrently learning hierarchical policies and subgoal representations. Although GCHRL possesses superior exploration ability by decomposing tasks via subgoals, existing GCHRL methods struggle in temporally extended tasks with sparse external rewards, since the high-level policy learning relies on external rewards. As the high-level policy selects subgoals in an online learned representation space, the dynamic change of the subgoal space severely hinders effective high-level exploration. In this paper, we propose a novel regularization that contributes to both stable and efficient subgoal representation learning. Building upon the stable representation, we design measures of novelty and potential for subgoals, and develop an active hierarchical exploration strategy that seeks out new promising subgoals and states without intrinsic rewards. Experimental results show that our approach significantly outperforms state-of-the-art baselines in continuous control tasks with sparse rewards. | ACTIVE HIERARCHICAL EXPLORATION WITH STABLE SUBGOAL REPRESENTATION LEARNING |
d263831649 | Transfer learning is a crucial technique for handling a small amount of data that is potentially related to other abundant data. However, most of the existing methods are focused on classification tasks using images and language datasets. Therefore, in order to expand the transfer learning scheme to regression tasks, we propose a novel transfer technique based on differential geometry, namely the Geometrically Aligned Transfer Encoder (GATE). In this method, we interpret the latent vectors from the model to exist on a Riemannian curved manifold. We find a proper diffeomorphism between pairs of tasks to ensure that every arbitrary point maps to a locally flat coordinate in the overlapping region, allowing the transfer of knowledge from the source to the target data. This also serves as an effective regularizer for the model to behave in extrapolation regions. In this article, we demonstrate that GATE outperforms conventional methods and exhibits stable behavior in both the latent space and extrapolation regions for various molecular graph datasets.Despite these achievements, the primary area of concern is mostly limited to classification tasks in the vision and language domains. Moreover, identifying an architecture that effectively addresses regression problems has proven to be challenging. Hence, our primary objective is to develop an algorithm that can be generally applied to regression problems. In this article, our main focus centers on molecular property prediction tasks[12,13,14,15,16,17,18]. Molecular datasets tend to be small in size, have a large number of task types, and are mainly regression. This makes molecular property prediction a good application to test our novel algorithm.The fundamental concept of our method rests on a geometric interpretation of the encoding space. Given that the encoding process within a model involves intricate non-linear mappings, it is reasonable * These authors contributed equally to this work Preprint. Under review. | Geometrically Aligned Transfer Encoder for Inductive Transfer in Regression Tasks |
d57721134 | We present an efficient convolution kernel for Convolutional Neural Networks (CNNs) on unstructured grids using parameterized differential operators while focusing on spherical signals such as panorama images or planetary signals. To this end, we replace conventional convolution kernels with linear combinations of differential operators that are weighted by learnable parameters. Differential operators can be efficiently estimated on unstructured grids using one-ring neighbors, and learnable parameters can be optimized through standard back-propagation. As a result, we obtain extremely efficient neural networks that match or outperform state-of-the-art network architectures in terms of performance but with a significantly smaller number of network parameters. We evaluate our algorithm in an extensive series of experiments on a variety of computer vision and climate science tasks, including shape classification, climate pattern segmentation, and omnidirectional image semantic segmentation. Overall, we (1) present a novel CNN approach on unstructured grids using parameterized differential operators for spherical signals, and(2)show that our unique kernel parameterization allows our model to achieve the same or higher accuracy with significantly fewer network parameters. | SPHERICAL CNNS ON UNSTRUCTURED GRIDS |
d18114929 | Vector representations of words have heralded a transformational approach to classical problems in NLP; the most popular example is word2vec. However, a single vector does not suffice to model the polysemous nature of many (frequent) words, i.e., words with multiple meanings. In this paper, we propose a three-fold approach for unsupervised polysemy modeling: (a) context representations, (b) sense induction and disambiguation and (c) lexeme (as a word and sense pair) representations. A key feature of our work is the finding that a sentence containing a target word is well represented by a low rank subspace, instead of a point in a vector space. We then show that the subspaces associated with a particular sense of the target word tend to intersect over a line (one-dimensional subspace), which we use to disambiguate senses using a clustering algorithm that harnesses the Grassmannian geometry of the representations. The disambiguation algorithm, which we call K-Grassmeans, leads to a procedure to label the different senses of the target word in the corpus -yielding lexeme vector representations, all in an unsupervised manner starting from a large (Wikipedia) corpus in English. Apart from several prototypical target (word,sense) examples and a host of empirical studies to intuit and justify the various geometric representations, we validate our algorithms on standard sense induction and disambiguation datasets and present new state-of-the-art results. | GEOMETRY OF POLYSEMY |
d252693198 | Predicting the binding structure of a small molecule ligand to a protein-a task known as molecular docking-is critical to drug design. Recent deep learning methods that treat docking as a regression problem have decreased runtime compared to traditional search-based methods but have yet to offer substantial improvements in accuracy. We instead frame molecular docking as a generative modeling problem and develop DIFFDOCK, a diffusion generative model over the non-Euclidean manifold of ligand poses. To do so, we map this manifold to the product space of the degrees of freedom (translational, rotational, and torsional) involved in docking and develop an efficient diffusion process on this space. Empirically, DIFFDOCK obtains a 38% top-1 success rate (RMSD<2Å) on PDB-Bind, significantly outperforming the previous state-of-the-art of traditional docking (23%) and deep learning (20%) methods. Moreover, while previous methods are not able to dock on computationally folded structures (maximum accuracy 10.4%), DIFFDOCK maintains significantly higher precision (21.7%). Finally, DIFFDOCK has fast inference times and provides confidence estimates with high selective accuracy. . Glide: a new approach for rapid, accurate docking and scoring. 2. enrichment factors in database screening. Journal of medicinal chemistry, 2004. | DIFFDOCK: DIFFUSION STEPS, TWISTS, AND TURNS FOR MOLECULAR DOCKING |
d256827051 | Synthesizing realistic animations of humans, animals, and even imaginary creatures, has long been a goal for artists and computer graphics professionals. Compared to the imaging domain, which is rich with large available datasets, the number of data instances for the motion domain is limited, particularly for the animation of animals and exotic creatures (e.g., dragons), which have unique skeletons and motion patterns. In this work, we present a Single Motion Diffusion Model, dubbed SinMDM, a model designed to learn the internal motifs of a single motion sequence with arbitrary topology and synthesize motions of arbitrary length that are faithful to them. We harness the power of diffusion models and present a denoising network explicitly designed for the task of learning from a single input motion. SinMDM is designed to be a lightweight architecture, which avoids overfitting by using a shallow network with local attention layers that narrow the receptive field and encourage motion diversity. SinMDM can be applied in various contexts, including spatial and temporal in-betweening, motion expansion, style transfer, and crowd animation. Our results show that SinMDM outperforms existing methods both in quality and time-space efficiency. Moreover, while current approaches require additional training for different applications, our work facilitates these applications at inference time. Our code and trained models are available at https://sinmdm.github.io/SinMDM-page. | Single Motion Diffusion |
d259936734 | Scaling Transformers to longer sequence lengths has been a major problem in the last several years, promising to improve performance in language modeling and high-resolution image understanding, as well as to unlock new applications in code, audio, and video generation. The attention layer is the main bottleneck in scaling to longer sequences, as its runtime and memory increase quadratically in the sequence length. FlashAttention [5] exploits the asymmetric GPU memory hierarchy to bring significant memory saving (linear instead of quadratic) and runtime speedup (2-4× compared to optimized baselines), with no approximation. However, FlashAttention is still not nearly as fast as optimized matrix-multiply (GEMM) operations, reaching only 25-40% of the theoretical maximum FLOPs/s. We observe that the inefficiency is due to suboptimal work partitioning between different thread blocks and warps on the GPU, causing either low-occupancy or unnecessary shared memory reads/writes. We propose FlashAttention-2, with better work partitioning to address these issues. In particular, we (1) tweak the algorithm to reduce the number of non-matmul FLOPs (2) parallelize the attention computation, even for a single head, across different thread blocks to increase occupancy, and (3) within each thread block, distribute the work between warps to reduce communication through shared memory. These yield around 2× speedup compared to FlashAttention, reaching 50-73% of the theoretical maximum FLOPs/s on A100 and getting close to the efficiency of GEMM operations. We empirically validate that when used end-to-end to train GPT-style models, FlashAttention-2 reaches training speed of up to 225 TFLOPs/s per A100 GPU (72% model FLOPs utilization). 1 | FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning |
d247292764 | Predicting molecular conformations from molecular graphs is a fundamental problem in cheminformatics and drug discovery. Recently, significant progress has been achieved with machine learning approaches, especially with deep generative models. Inspired by the diffusion process in classical non-equilibrium thermodynamics where heated particles will diffuse from original states to a noise distribution, in this paper, we propose a novel generative model named GEODIFF for molecular conformation prediction. GEODIFF treats each atom as a particle and learns to directly reverse the diffusion process (i.e., transforming from a noise distribution to stable conformations) as a Markov chain. Modeling such a generation process is however very challenging as the likelihood of conformations should be rototranslational invariant. We theoretically show that Markov chains evolving with equivariant Markov kernels can induce an invariant distribution by design, and further propose building blocks for the Markov kernels to preserve the desirable equivariance property. The whole framework can be efficiently trained in an end-toend fashion by optimizing a weighted variational lower bound to the (conditional) likelihood. Experiments on multiple benchmarks show that GEODIFF is superior or comparable to existing state-of-the-art approaches, especially on large molecules. 1 | GEODIFF: A GEOMETRIC DIFFUSION MODEL FOR MOLECULAR CONFORMATION GENERATION |
d203736530 | Recent research shows that the following two models are equivalent: (a) infinitely wide neural networks (NNs) trained under ℓ2 loss by gradient descent with infinitesimally small learning rate (b) kernel regression with respect to so-called Neural Tangent Kernels (NTKs)(Jacot et al., 2018). An efficient algorithm to compute the NTK, as well as its convolutional counterparts, appears inArora et al. (2019a), which allowed studying performance of infinitely wide nets on datasets like CIFAR-10. However, superquadratic running time of kernel methods makes them best suited for small-data tasks. We report results suggesting neural tangent kernels perform strongly on low-data tasks. 1. On a standard testbed of classification/regression tasks from the UCI database, NTK SVM beats the previous gold standard, Random Forests (RF), and also the corresponding finite nets. 2. On CIFAR-10 with 10 -640 training samples, Convolutional NTK consistently beats ResNet-34 by 1% -3%. 3. On VOC07 testbed for few-shot image classification tasks on ImageNet with transfer learning(Goyal et al., 2019), replacing the linear SVM currently used with a Convolutional NTK SVM consistently improves performance. 4. Comparing the performance of NTK with the finite-width net it was derived from, NTK behavior starts at lower net widths than suggested by theoretical analysis(Arora et al., 2019a). NTK's efficacy may trace to lower variance of output.Ironically, while the above-mentioned analysis, at first sight, appears to reduce the study of a complicated model -deep networks -to an older, simpler model -kernel regression -in practice the simpler model is computationally less efficient because running time of kernel regression can be quadratic in the number of data points! 1 Thus computing using CNTK kernel on large datasets like ImageNet currently appears infeasible. Even on CIFAR-10, it seems infeasible to incorporate data augmentation.However, kernel classifiers are very efficient on small datasets. Here NTKs could conceivably be practical while at the same time bringing some of the power of deep networks to these settings. We recall that recentlyOlson et al. (2018)showed that multilayer neural networks can be reasonably effective on small datasets, specifically on a UCI testbed of tasks with as few as dozens of training examples. Of course, this required some hyperparameter tuning, although they noted that such tuning is also needed for the champion method, Random Forests (RF), which multilayer neural networks could not beat.It is thus natural to check if NTK -corresponding to infinitely wide fully-connected networks -performs well in such small-data tasks 2 . Convex objectives arising from kernels have stable solvers with minimal hyperparameter tuning. Furthermore, random initialization in deep network training seems to lead to higher variance in the output, which can hurt performance in small-data settings. Can NTK's do better? Below we will see that in the setup of Olson et al.(2018), NTK predictors indeed outperforms corresponding finite deep networks, and also slightly beats the earlier gold standard, Random Forests. This suggests NTK predictors should belong in any list of off-the-shelf machine learning methods.Following are low-data settings where we used NTKs and CNTKs: • In the testbed of 90 classification tasks from UCI database, NTK predictor achieves superior, and arguably the strongest classification performance. This is verified via several standard statistical tests, including Friedman Rank, Average Accuracy, Percentage of the Maximum Accuracy (PMA) and probability of achieving 90%/95% maximum accuracy (P90 and P95), performed to compare performances of different classifiers on 90 datasets from UCI database. (The authors plan to release the code, to allow off-the-shelf use of this method. It does not require GPUs.) • We find the performance of NN is close to that of NTK. On every dataset from UCI database, the difference between the classification accuracy of NN and that of NTK is within 5%. On the other hand, on some datasets, the difference between classification accuracy of NN (or NTK) and that of other classifiers like RF can be as high as 20%. This indicates in low-data settings, NTK is indeed a good description of NN. Furthermore, we find NTK is more stable (smaller variance), which seems to help it achieve better accuracy on small datasets (cf.Figure 2b). • CNTK is useful in computer vision tasks with small-data. On CIFAR-10, we compare CNTK with ResNet using 10 -640 training samples and find CNTK can beat ResNet by 1% − 3%. We further study few-shot image classification task on VOC07 dataset. The standard method is to first use a pre-trained network, e.g., ResNet-50 trained on ImageNet, to extract features and then directly apply a linear classifier on the extracted features(Goyal et al., 2019). Here we replace the linear classifier with CNTK and obtain better classification accuracy in various setups.Paper organization. Section 2 discusses related work. Section 3 reviews the derivation of NTK. Section 4 presents experiments using NN and NTK on UCI datasets. Section 5 presents experiments using CNN and CNTK on small CIFAR-10 datasets. Section 6 presents experiments using CNTK for the few-shot learning setting. Additional technical details are presented in appendix.Related WorkOur paper is inspired byFernández-Delgado et al. (2014)which conducted extensive experiments on UCI dataset. Their conclusion is random forest performs the best, which is followed by the SVM with Gaussian 1 The bottleneck is constructing the kernel, which scales quadratically with the number of data points (Arora et al., 2019a). The regression also requires matrix inversion, which can be cubic in the number of data points.2 Note that NTKs can also be used in kernel SVMs, which are not known to be equivalent to training infinitely wide networks. Currently, equivalence is only known for ridge regression. We tried both. | Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks |
d51979536 | Reinforcement learning algorithms rely on carefully engineering environment rewards that are extrinsic to the agent. However, annotating each environment with hand-designed, dense rewards is not scalable, motivating the need for developing reward functions that are intrinsic to the agent. Curiosity is a type of intrinsic reward function which uses prediction error as reward signal. In this paper: (a) We perform the first large-scale study of purely curiosity-driven learning, i.e. without any extrinsic rewards, across 54 standard benchmark environments, including the Atari game suite. Our results show surprisingly good performance, and a high degree of alignment between the intrinsic curiosity objective and the handdesigned extrinsic rewards of many game environments. (b) We investigate the effect of using different feature spaces for computing prediction error and show that random features are sufficient for many popular RL game benchmarks, but learned features appear to generalize better (e.g. to novel game levels in Super Mario Bros.). (c) We demonstrate limitations of the prediction-based rewards in stochastic setups. Game-play videos and code are at https://pathak22.github. io/large-scale-curiosity/. * Alphabetical ordering; the first three authors contributed equally.Preprint. Work in progress. | Large-Scale Study of Curiosity-Driven Learning |
d235795764 | Source-free domain adaptation (SFDA) aims to adapt a model trained on labelled data in a source domain to unlabelled data in a target domain without access to the source-domain data during adaptation. Existing methods for SFDA leverage entropy-minimization techniques which: (i) apply only to classification; (ii) destroy model calibration; and (iii) rely on the source model achieving a good level of feature-space class-separation in the target domain. We address these issues for a particularly pervasive type of domain shift called measurement shift which can be resolved by restoring the source features rather than extracting new ones. In particular, we propose Feature Restoration (FR) wherein we: (i) store a lightweight and flexible approximation of the feature distribution under the source data; and (ii) adapt the feature-extractor such that the approximate feature distribution under the target data realigns with that saved on the source. We additionally propose a bottomup training scheme which boosts performance, which we call Bottom-Up Feature Restoration (BUFR). On real and synthetic data, we demonstrate that BUFR outperforms existing SFDA methods in terms of accuracy, calibration, and data efficiency, while being less reliant on the performance of the source model in the target domain.Recently, there has been increasing interest in methods to address this setting of source-free domain adaptation (SFDA, Kundu et al. 2020; Liang et al. 2020; Li et al. 2020; Morerio et al. 2020) where the source dataset is unavailable during adaptation in the deployment phase. However, to adapt to the target domain, most of these methods employ entropy-minimization techniques which: (i) apply only to classification (discrete labels); (ii) destroy model calibration-minimizing prediction-entropy causes every sample to be classified (correctly or incorrectly) with extreme confidence; and (iii) assume that, in the target domain, the feature space of the unadapted source model contains reasonably well-separated data clusters, where samples within a cluster tend to share the same class label. As . Gradient-based learning applied to document recognition. unifying view on dataset shift in classification. Pattern Recognition, 45:521-530, 2012.Pietro Morerio, Riccardo Volpi, Ruggero Ragonesi, and Vittorio Murino. Generative pseudo-label refinement for unsupervised domain adaptation. In Tangent prop-a formalism for specifying selected invariances in an adaptive network. In Advances in Neural Information Processing Systems, pp. 895-903, 1991.Serban Stan and Mohammad Rostami. Unsupervised model adaptation for continual semantic segmentation. In Towards robust CNN-based object detection through augmentation with synthetic rain variations. | SOURCE-FREE ADAPTATION TO MEASUREMENT SHIFT VIA BOTTOM-UP FEATURE RESTORATION |
d225040049 | We study the problem of policy optimization for infinite-horizon discounted Markov Decision Processes with softmax policy and nonlinear function approximation trained with policy gradient algorithms. We concentrate on the training dynamics in the mean-field regime, modeling e.g., the behavior of wide single hidden layer neural networks, when exploration is encouraged through entropy regularization. The dynamics of these models is established as a Wasserstein gradient flow of distributions in parameter space. We further prove global optimality of the fixed points of this dynamics under mild conditions on their initialization.A exp(f w (s, a))da and parametrize the energy f as a two-layer neural network in the mean-field regime, i.e., | Global optimality of softmax policy gradient with single hidden layer neural networks in the mean-field regime |
d11663659 | Probabilistic models with discrete latent variables naturally capture datasets composed of discrete classes. However, they are difficult to train efficiently, since backpropagation through discrete variables is generally not possible. We present a novel method to train a class of probabilistic models with discrete latent variables using the variational autoencoder framework, including backpropagation through the discrete latent variables. The associated class of probabilistic models comprises an undirected discrete component and a directed hierarchical continuous component. The discrete component captures the distribution over the disconnected smooth manifolds induced by the continuous component. As a result, this class of models efficiently learns both the class of objects in an image, and their specific realization in pixels, from unsupervised data; and outperforms state-ofthe-art methods on the permutation-invariant MNIST, Omniglot, and Caltech-101 Silhouettes datasets. | DISCRETE VARIATIONAL AUTOENCODERS |
d259298536 | The monotonic dependence of the outputs of a neural network on some of its inputs is a crucial inductive bias in many scenarios where domain knowledge dictates such behavior.This is especially important for interpretability and fairness considerations.In a broader context, scenarios in which monotonicity is important can be found in finance, medicine, physics, and other disciplines.It is thus desirable to build neural network architectures that implement this inductive bias provably.In this work, we propose a weight-constrained architecture 1 with a single residual connection to achieve exact monotonic dependence in any subset of the inputs.The weight constraint scheme directly controls the Lipschitz constant of the neural network and thus provides the additional benefit of robustness.Compared to currently existing techniques used for monotonicity, our method is simpler in implementation and in theory foundations, has negligible computational overhead, is guaranteed to produce monotonic dependence, and is highly expressive.We show how the algorithm is used to train powerful, robust, and interpretable discriminators that achieve competitive performance compared to current state-of-the-art methods across various benchmarks, from social applications to the classification of the decays of subatomic particles produced at the CERN Large Hadron Collider. | EXPRESSIVE MONOTONIC NEURAL NETWORKS |
d259108646 | Image recognition and generation have long been developed independently of each other. With the recent trend towards general-purpose representation learning, the development of general representations for both recognition and generation tasks is also promoted. However, preliminary attempts mainly focus on generation performance, but are still inferior on recognition tasks. These methods are modeled in the vector-quantized (VQ) space, whereas leading recognition methods use pixels as inputs. Our key insights are twofold: (1) pixels as inputs are crucial for recognition tasks; (2) VQ tokens as reconstruction targets are beneficial for generation tasks. These observations motivate us to propose an Alternating Denoising Diffusion Process (ADDP) that integrates these two spaces within a single representation learning framework. In each denoising step, our method first decodes pixels from previous VQ tokens, then generates new VQ tokens from the decoded pixels. The diffusion process gradually masks out a portion of VQ tokens to construct the training samples. The learned representations can be used to generate diverse high-fidelity images and also demonstrate excellent transfer performance on recognition tasks. Extensive experiments show that our method achieves competitive performance on unconditional generation, ImageNet classification, COCO detection, and ADE20k segmentation. Importantly, our method represents the first successful development of general representations applicable to both generation and dense recognition tasks. Code shall be released. | ADDP: Learning General Representations for Image Recognition and Generation with Alternating Denoising Diffusion Process |
d202749904 | Generative adversarial networks have seen rapid development in recent years and have led to remarkable improvements in generative modelling of images. However, their application in the audio domain has received limited attention, and autoregressive models, such as WaveNet, remain the state of the art in generative modelling of audio signals such as human speech. To address this paucity, we introduce GAN-TTS, a Generative Adversarial Network for Text-to-Speech. Our architecture is composed of a conditional feed-forward generator producing raw speech audio, and an ensemble of discriminators which operate on random windows of different sizes. The discriminators analyse the audio both in terms of general realism, as well as how well the audio corresponds to the utterance that should be pronounced. To measure the performance of GAN-TTS, we employ both subjective human evaluation (MOS -Mean Opinion Score), as well as novel quantitative metrics (Fréchet DeepSpeech Distance and Kernel DeepSpeech Distance), which we find to be well correlated with MOS. We show that GAN-TTS is capable of generating high-fidelity speech with naturalness comparable to the state-of-the-art models, and unlike autoregressive models, it is highly parallelisable thanks to an efficient feed-forward generator. Listen to GAN-TTS reading this abstract at https://storage.googleapis.com/ deepmind-media/research/abstract.wav. | HIGH FIDELITY SPEECH SYNTHESIS WITH ADVERSARIAL NETWORKS |
d247011606 | Recent success in cooperative multi-agent reinforcement learning (MARL) relies on centralized training and policy sharing. Centralized training eliminates the issue of non-stationarity MARL yet induces large communication costs, and policy sharing is empirically crucial to efficient learning in certain tasks yet lacks theoretical justification. In this paper, we formally characterize a subclass of cooperative Markov games where agents exhibit a certain form of homogeneity such that policy sharing provably incurs no suboptimality. This enables us to develop the first consensus-based decentralized actor-critic method where the consensus update is applied to both the actors and the critics while ensuring convergence. We also develop practical algorithms based on our decentralized actor-critic method to reduce the communication cost during training, while still yielding policies comparable with centralized training. | COMMUNICATION-EFFICIENT ACTOR-CRITIC METHODS FOR HOMOGENEOUS MARKOV GAMES |
d203836132 | When humans observe a physical system, they can easily locate objects, understand their interactions, and anticipate future behavior. For computers, however, learning such models from videos in an unsupervised fashion is an unsolved research problem. In this paper, we present STOVE, a novel state-space model for videos, which explicitly reasons about objects and their positions, velocities, and interactions. It is constructed by combining an image model and a dynamics model in compositional manner and improves on previous work by reusing the dynamics model for inference, accelerating and regularizing training. STOVE predicts videos with convincing physical behavior over thousands of timesteps, outperforms previous unsupervised models, and even approaches the performance of supervised baselines. We further demonstrate the strength of our model as a simulator for sample efficient model-based control in a task with heavily interacting objects. * Both authors contributed equally to this work. | STRUCTURED OBJECT-AWARE PHYSICS PREDICTION FOR VIDEO MODELING AND PLANNING |
d245837784 | Federated learning is a popular technology for training machine learning models on distributed data sources without sharing data. Vertical federated learning or feature-based federated learning applies to the cases that different data sources share the same sample ID space but differ in feature space. To ensure the data owners' long-term engagement, it is critical to objectively assess the contribution from each data source and recompense them accordingly. The Shapley value (SV) is a provably fair contribution valuation metric originated from cooperative game theory. However, computing the SV requires extensively retraining the model on each subset of data sources, which causes prohibitively high communication costs in federated learning. We propose a contribution valuation metric called vertical federated Shapley value (VerFedSV) based on SV. We show that VerFedSV not only satisfies many desirable properties for fairness but is also efficient to compute, and can be adapted to both synchronous and asynchronous vertical federated learning algorithms. Both theoretical analysis and extensive experimental results verify the fairness, efficiency, and adaptability of VerFedSV. | Fair and efficient contribution valuation for vertical federated learning |
d257353252 | Deep learning has had tremendous success at learning low-dimensional representations of high-dimensional data. This success would be impossible if there was no hidden low-dimensional structure in data of interest; this existence is posited by the manifold hypothesis, which states that the data lies on an unknown manifold of low intrinsic dimension. In this paper, we argue that this hypothesis does not properly capture the low-dimensional structure typically present in image data. Assuming that data lies on a single manifold implies intrinsic dimension is identical across the entire data space, and does not allow for subregions of this space to have a different number of factors of variation. To address this deficiency, we consider the union of manifolds hypothesis, which states that data lies on a disjoint union of manifolds of varying intrinsic dimensions. We empirically verify this hypothesis on commonlyused image datasets, finding that indeed, observed data lies on a disconnected set and that intrinsic dimension is not constant. We also provide insights into the implications of the union of manifolds hypothesis in deep learning, both supervised and unsupervised, showing that designing models with an inductive bias for this structure improves performance across classification and generative modelling tasks. Our code is available at https://github.com/layer6ai-labs/UoMH. * Work done during an internship at Layer 6 AI. | VERIFYING THE UNION OF MANIFOLDS HYPOTHESIS FOR IMAGE DATA |
d240288595 | In this paper, we propose a novel Collaborative Pure Exploration in Kernel Bandit model (CoPE-KB), where multiple agents collaborate to complete different but related tasks with limited communication. Our model generalizes prior CoPE formulation with the single-task and classic MAB setting to allow multiple tasks and general reward structures. We propose a novel communication scheme with an efficient kernelized estimator, and design algorithms CoKernelFC and CoKernelFB for CoPE-KB with fixed-confidence and fixed-budget objectives, respectively. Sample and communication complexities are provided to demonstrate the efficiency of our algorithms. Our theoretical results explicitly quantify how task similarities influence learning speedup, and only depend on the effective dimension of feature space. Our novel techniques, such as an efficient kernelized estimator and decomposition of task similarities and arm features, which overcome the communication difficulty in high-dimensional feature space and reveal the impacts of task similarities on sample complexity, can be of independent interests. | COLLABORATIVE PURE EXPLORATION IN KERNEL BANDIT |
d246634117 | Catastrophic forgetting is one of the major challenges in continual learning. To address this issue, some existing methods put restrictive constraints on the optimization space of the new task for minimizing the interference to old tasks. However, this may lead to unsatisfactory performance for the new task, especially when the new task is strongly correlated with old tasks. To tackle this challenge, we propose Trust Region Gradient Projection (TRGP) for continual learning to facilitate the forward knowledge transfer based on an efficient characterization of task correlation. Particularly, we introduce a notion of 'trust region' to select the most related old tasks for the new task in a layer-wise and single-shot manner, using the norm of gradient projection onto the subspace spanned by task inputs. Then, a scaled weight projection is proposed to cleverly reuse the frozen weights of the selected old tasks in the trust region through a layer-wise scaling matrix. By jointly optimizing the scaling matrices and the model, where the model is updated along the directions orthogonal to the subspaces of old tasks, TRGP can effectively prompt knowledge transfer without forgetting. Extensive experiments show that our approach achieves significant improvement over related state-of-the-art methods. | TRGP: TRUST REGION GRADIENT PROJECTION FOR CONTINUAL LEARNING |
d3485693 | We consider the problem of learning a one-hidden-layer neural network: we assume the input x ∈ R d is from Gaussian distribution and the label y = a ⊤ σ(Bx) + ξ, where a is a nonnegative vector in R m with m ≤ d, B ∈ R m×d is a full-rank weight matrix, and ξ is a noise vector. We first give an analytic formula for the population risk of the standard squared loss and demonstrate that it implicitly attempts to decompose a sequence of low-rank tensors simultaneously.Inspired by the formula, we design a non-convex objective function G(·) whose landscape is guaranteed to have the following properties:1. All local minima of G are also global minima. 2. All global minima of G correspond to the ground truth parameters. 3. The value and gradient of G can be estimated using samples.With these properties, stochastic gradient descent on G provably converges to the global minimum and learn the ground-truth parameters. We also prove finite sample complexity result and validate the results by simulations. | Learning One-hidden-layer Neural Networks with Landscape Design |
d204785044 | Equivariance is a nice property to have as it produces much more parameter efficient neural architectures and preserves the structure of the input through the feature mapping. Even though some combinations of transformations might never appear (e.g., an upright face with a horizontal nose), current equivariant architectures consider the set of all possible transformations in a transformation group when learning feature representations. Contrarily, the human visual system is able to attend to the set of relevant transformations occurring in the environment and utilizes this information to assist and improve object recognition. Based on this observation, we modify conventional equivariant feature mappings such that they are able to attend to the set of co-occurring transformations in data and generalize this notion to act on groups consisting of multiple symmetries. We show that our proposed co-attentive equivariant neural networks consistently outperform conventional rotation equivariant and rotation & reflection equivariant neural networks on rotated MNIST and CIFAR-10. | CO-ATTENTIVE EQUIVARIANT NEURAL NETWORKS: FOCUSING EQUIVARIANCE ON TRANSFORMATIONS CO-OCCURRING IN DATA |
d260203143 | We introduce new jailbreak attacks on vision language models (VLMs), which use aligned LLMs and are resilient to text-only jailbreak attacks.Specifically, we develop cross-modality attacks on alignment where we pair adversarial images going through the vision encoder with textual prompts to break the alignment of the language model.Our attacks employ a novel compositional strategy that combines an image, adversarially targeted towards toxic embeddings, with generic prompts to accomplish the jailbreak.Thus, the LLM draws the context to answer the generic prompt from the adversarial image.The generation of benign-appearing adversarial images leverages a novel embedding-space-based methodology, operating with no access to the LLM model.Instead, the attacks require access only to the vision encoder and utilize one of our four embedding space targeting strategies.By not requiring access to the LLM, the attacks lower the entry barrier for attackers, particularly when vision encoders such as CLIP are embedded in closed-source LLMs.The attacks achieve a high success rate across different VLMs, highlighting the risk of cross-modality alignment vulnerabilities, and the need for new alignment approaches for multi-modal models.Content warning: We provide illustrative adversarial attack examples to reveal the generative models' vulnerabilities, aiming to aid the development of robust models to adversarial attacks. | JAILBREAK IN PIECES: COMPOSITIONAL ADVERSARIAL ATTACKS ON MULTI-MODAL LANGUAGE MODELS |
d257280083 | Unsupervised meta-learning aims to learn generalizable knowledge across a distribution of tasks constructed from unlabeled data. Here, the main challenge is how to construct diverse tasks for meta-learning without label information; recent works have proposed to create, e.g., pseudo-labeling via pretrained representations or creating synthetic samples via generative models. However, such a task construction strategy is fundamentally limited due to heavy reliance on the immutable pseudo-labels during meta-learning and the quality of the representations or the generated samples. To overcome the limitations, we propose a simple yet effective unsupervised meta-learning framework, coined Pseudo-supervised Contrast (PsCo), for few-shot classification. We are inspired by the recent self-supervised learning literature; PsCo utilizes a momentum network and a queue of previous batches to improve pseudo-labeling and construct diverse tasks in a progressive manner. Our extensive experiments demonstrate that PsCo outperforms existing unsupervised meta-learning methods under various in-domain and cross-domain few-shot classification benchmarks. We also validate that PsCo is easily scalable to a large-scale benchmark, while recent prior-art meta-schemes are not.Figure 1: An overview of the proposed Pseudo-supervised Contrast (PsCo). PsCo constructs an Nway K-shot few-shot classification task using the current mini-batch {x i } and the queue of previous mini-batches; and then, it learns the task via contrastive learning. Here, A is a label assignment matrix found by the Sinkhorn-Knopp algorithm (Cuturi, 2013), A is a pre-defined augmentation distribution, f is a backbone feature extractor, g and h are projection and prediction MLPs, respectively, and φ is an exponential moving average (EMA) of the model parameter θ.To overcome the limitations of the existing UML approaches, in this paper, we ask whether one can (a) progressively improve a pseudo-labeling strategy during meta-learning, and (b) construct more diverse tasks without generative models. We draw inspiration from recent advances in selfsupervised learning literature(He et al., 2020;Khosla et al., 2020), which has shown remarkable success in representation learning without labeled data. In particular, we utilize (a) a momentum network to improve pseudo-labeling progressively via temporal ensemble; and (b) a momentum queue to construct diverse tasks using previous mini-batches in an online manner.Formally, we propose Pseudo-supervised Contrast (PsCo), a novel and effective unsupervised metalearning framework, for few-shot classification. Our key idea is to construct few-shot classification tasks using the current and previous mini-batches based on the momentum network and the momentum queue. Specifically, given a random mini-batch of N unlabeled samples, we treat them as N queries (i.e., test samples) of different N labels, and then select K shots (i.e., training samples) for each label from the queue of previous mini-batches based on representations extracted by the momentum network. To further improve the selection procedure, we utilize top-K sampling after applying a matching algorithm, Sinkhorn-Knopp (Cuturi, 2013). Finally, we optimize our model via supervised contrastive learning(Khosla et al., 2020)for solving the N -way K-shot task. Remark that our few-shot task construction relies on not only the current mini-batch but also the momentum network and the queue of previous mini-batches. Therefore, our task construction (i.e., pseudo-labeling) strategy (a) is progressively improved during meta-learning with the momentum network, and (b) constructs diverse tasks since the shots can be selected from the entire dataset. Our framework is illustrated inFigure 1.Throughout extensive experiments, we demonstrate the effectiveness of the proposed framework, PsCo, under various few-shot classification benchmarks. First, PsCo achieves state-of-the-art performance under both Omniglot (Lake et al., 2011) and miniImageNet (Ravi & Larochelle, 2017) few-shot benchmarks; its performance is even competitive with supervised meta-learning methods. Next, PsCo also shows superiority under cross-domain few-shot learning scenarios. Finally, we demonstrate that PsCo is scalable to a large-scale benchmark, ImageNet(Deng et al., 2009).We summarize our contributions as follows:• We propose PsCo, an effective unsupervised meta-learning (UML) framework for few-shot classification, which constructs diverse few-shot pseudo-tasks without labels utilizing the momentum network and the queue of previous batches in a progressive manner. | UNSUPERVISED META-LEARNING VIA FEW-SHOT PSEUDO-SUPERVISED CONTRASTIVE LEARNING |
d253224274 | We introduce Noise Injection Node Regularization (NINR), a method of injecting structured noise into Deep Neural Networks (DNN) during the training stage, resulting in an emergent regularizing effect.We present theoretical and empirical evidence for substantial improvement in robustness against various test data perturbations for feed-forward DNNs when trained under NINR.The novelty in our approach comes from the interplay of adaptive noise injection and initialization conditions such that noise is the dominant driver of dynamics at the start of training.As it simply requires the addition of external nodes without altering the existing network structure or optimization algorithms, this method can be easily incorporated into many standard problem specifications.We find improved stability against a number of data perturbations, including domain shifts, with the most dramatic improvement obtained for unstructured noise, where our technique outperforms other existing methods such as Dropout or L 2 regularization, in some cases.We further show that desirable generalization properties on clean data are generally maintained. | NOISE INJECTION NODE REGULARIZATION FOR ROBUST LEARNING |
d208548538 | Formal verification of neural networks is essential for their deployment in safetycritical areas. Many available formal verification methods have been shown to be instances of a unified Branch and Bound (BaB) formulation. We propose a novel framework for designing an effective branching strategy for BaB. Specifically, we learn a graph neural network (GNN) to imitate the strong branching heuristic behaviour. Our framework differs from previous methods for learning to branch in two main aspects. Firstly, our framework directly treats the neural network we want to verify as a graph input for the GNN. Secondly, we develop an intuitive forward and backward embedding update schedule. Empirically, our framework achieves roughly 50% reduction in both the number of branches and the time required for verification on various convolutional networks when compared to the best available hand-designed branching strategy. In addition, we show that our GNN model enjoys both horizontal and vertical transferability. Horizontally, the model trained on easy properties performs well on properties of increased difficulty levels. Vertically, the model trained on small neural networks achieves similar performance on large neural networks. Code for all experiments is available at | NEURAL NETWORK BRANCHING FOR NEURAL NETWORK VERIFICATION |
d59310641 | Self-attention is a useful mechanism to build generative models for language and images. It determines the importance of context elements by comparing each element to the current time step. In this paper, we show that a very lightweight convolution can perform competitively to the best reported self-attention results. Next, we introduce dynamic convolutions which are simpler and more efficient than self-attention. We predict separate convolution kernels based solely on the current time-step in order to determine the importance of context elements. The number of operations required by this approach scales linearly in the input length, whereas self-attention is quadratic. Experiments on large-scale machine translation, language modeling and abstractive summarization show that dynamic convolutions improve over strong self-attention models. On the WMT'14 English-German test set dynamic convolutions achieve a new state of the art of 29.7 BLEU. 1 * Work done during an internship at Facebook. 1 Code and pre-trained models available at | PAY LESS ATTENTION WITH LIGHTWEIGHT AND DYNAMIC CONVOLUTIONS |
d252762544 | Dynamic benchmarks interweave model fitting and data collection in an attempt to mitigate the limitations of static benchmarks. In contrast to an extensive theoretical and empirical study of the static setting, the dynamic counterpart lags behind due to limited empirical studies and no apparent theoretical foundation to date. Responding to this deficit, we initiate a theoretical study of dynamic benchmarking. We examine two realizations, one capturing current practice and the other modeling more complex settings. In the first model, where data collection and model fitting alternate sequentially, we prove that model performance improves initially but can stall after only three rounds. Label noise arising from, for instance, annotator disagreement leads to even stronger negative results. Our second model generalizes the first to the case where data collection and model fitting have a hierarchical dependency structure. We show that this design guarantees strictly more progress than the first, albeit at a significant increase in complexity. We support our theoretical analysis by simulating dynamic benchmarks on two popular datasets. These results illuminate the benefits and practical limitations of dynamic benchmarking, providing both a theoretical foundation and a causal explanation for observed bottlenecks in empirical work. arXiv:2210.03165v3 [cs.LG] 1 Mar 2023 1. Model building: Given a distribution, find an approximate risk minimizer. 2. Data collection: Given a model, find a new distribution. 3. Model combination: Combine a set of models into a single model. 4. Data combination: Combine a set of distributions into a single distribution. | A THEORY OF DYNAMIC BENCHMARKS |
d263829270 | Learning a precise dynamics model can be crucial for offline reinforcement learning, which, unfortunately, has been found to be quite challenging.Dynamics models that are learned by fitting historical transitions often struggle to generalize to unseen transitions.In this study, we identify a hidden but pivotal factor termed dynamics reward that remains consistent across transitions, offering a pathway to better generalization.Therefore, we propose the idea of reward-consistent dynamics models: any trajectory generated by the dynamics model should maximize the dynamics reward derived from the data.We implement this idea as the MOREC (Model-based Offline reinforcement learning with Reward Consistency) method, which can be seamlessly integrated into previous offline model-based reinforcement learning (MBRL) methods.MOREC learns a generalizable dynamics reward function from offline data, which is subsequently employed as a transition filter in any offline MBRL method: when generating transitions, the dynamics model generates a batch of transitions and selects the one with the highest dynamics reward value.On a synthetic task, we visualize that MOREC has a strong generalization ability and can surprisingly recover some distant unseen transitions.On 21 offline tasks in D4RL and NeoRL benchmarks, MOREC improves the previous state-of-the-art performance by a significant margin, i.e., 4.6% on D4RL tasks and 25.9% on NeoRL tasks.Notably, MOREC is the first method that can achieve above 95% online RL performance in 6 out of 12 D4RL tasks and 3 out of 9 NeoRL tasks.* CQL (Kumar et al., 2020) adds penalization to Q-values for the samples out of distribution; * TD3+BC (Fujimoto & Gu, 2021) incorporates a BC regularization term into the policy optimization objective; * EDAC (An et al., 2021) proposed to penalize based on the uncertainty degree of the Q-value.Model-based offline RL. * COMBO(Yu et al., 2021) which applies CQL in dyna-style enforces Q-values small on OOD samples; * RAMBO(Rigter et al., 2022) trains the dynamics model adversarially to minimize the value function without loss of accuracy on the transition prediction; * MOPO (Yu et al., 2020) learns a pessimistic value function from rewards penalized with the uncertainty of the dynamics model's prediction; * MOBILE (Sun et al., 2023) penalizes the rewards with uncertainty quantified by the inconsistency of Bellman estimations under an ensemble of learned dynamics models. | REWARD-CONSISTENT DYNAMICS MODELS ARE STRONGLY GENERALIZABLE FOR OFFLINE REINFORCEMENT LEARNING |
d53104061 | Weight decay is one of the standard tricks in the neural network toolbox, but the reasons for its regularization effect are poorly understood, and recent results have cast doubt on the traditional interpretation in terms of L 2 regularization. Literal weight decay has been shown to outperform L 2 regularization for optimizers for which they differ. We empirically investigate weight decay for three optimization algorithms (SGD, Adam, and K-FAC) and a variety of network architectures. We identify three distinct mechanisms by which weight decay exerts a regularization effect, depending on the particular optimization algorithm and architecture: (1) increasing the effective learning rate, (2) approximately regularizing the inputoutput Jacobian norm, and (3) reducing the effective damping coefficient for second-order optimization. Our results provide insight into how to improve the regularization of neural networks. | THREE MECHANISMS OF WEIGHT DECAY REGULARIZATION |
d219558527 | We study the problem of few-sample fine-tuning of BERT contextual representations, and identify three sub-optimal choices in current, broadly adopted practices. First, we observe that the omission of the gradient bias correction in the BERTADAM optimizer results in fine-tuning instability. We also find that parts of the BERT network provide a detrimental starting point for fine-tuning, and simply re-initializing these layers speeds up learning and improves performance. Finally, we study the effect of training time, and observe that commonly used recipes often do not allocate sufficient time for training. In light of these findings, we re-visit recently proposed methods to improve few-sample fine-tuning with BERT and re-evaluate their effectiveness. Generally, we observe a decrease in their relative impact when modifying the fine-tuning process based on our findings. | Revisiting Few-sample BERT Fine-tuning |
d225039786 | Offline reinforcement learning seeks to utilize offline (observational) data to guide the learning of (causal) sequential decision making strategies. The hope is that offline reinforcement learning coupled with function approximation methods (to deal with the curse of dimensionality) can provide a means to help alleviate the excessive sample complexity burden in modern sequential decision making problems. However, the extent to which this broader approach can be effective is not well understood, where the literature largely consists of sufficient conditions. This work focuses on the basic question of what are necessary representational and distributional conditions that permit provable sample-efficient offline reinforcement learning. Perhaps surprisingly, our main result shows that even if: i) we have realizability in that the true value function of every policy is linear in a given set of features and 2) our off-policy data has good coverage over all features (under a strong spectral condition), then any algorithm still (information-theoretically) requires a number of offline samples that is exponential in the problem horizon in order to non-trivially estimate the value of any given policy. Our results highlight that sample-efficient offline policy evaluation is simply not possible unless significantly stronger conditions hold; such conditions include either having low distribution shift (where the offline data distribution is close to the distribution of the policy to be evaluated) or significantly stronger representational conditions (beyond realizability). | What are the Statistical Limits of Offline RL with Linear Function Approximation? |
d259847777 | Generative adversarial networks (GANs) learn a target probability distribution by optimizing a generator and a discriminator with minimax objectives. This paper addresses the question of whether such optimization actually provides the generator with gradients that make its distribution close to the target distribution. We derive metrizable conditions, sufficient conditions for the discriminator to serve as the distance between the distributions by connecting the GAN formulation with the concept of sliced optimal transport. Furthermore, by leveraging these theoretical results, we propose a novel GAN training scheme, called slicing adversarial network (SAN). With only simple modifications, a broad class of existing GANs can be converted to SANs. Experiments on synthetic and image datasets support our theoretical results and the SAN's effectiveness as compared to usual GANs. Furthermore, we also apply SAN to StyleGAN-XL, which leads to state-of-the-art FID score amongst GANs for class conditional generation on ImageNet 256×256. arXiv:2301.12811v3 [cs.LG] 6 Sep 2023 Preprint Table 1: Common GAN losses do not simultaneously satisfy all the sufficient conditions given in Theorem 5.3. Thus, we propose the SAN to address one of the conditions, direction optimality. Even if a direction ω is the maximizer of the inner problems V, it does not satisfy direction optimality except in Wasserstein GAN (see Sec. 6). The results in Appx. E empirically demonstrate that a discriminator trained on Wasserstein GAN tends not to satisfy separability. The last condition of injectivity depends on the discriminator implementation (see Appx. E for empirical verification). Direction optimality Separability Injectivity Wassertein GAN ✓ weak * GAN (Hinge, Saturating, Non-saturating) ✗ ✓ * SAN (Hinge, Saturating, Non-saturating) ✓ ✓ *In this paper, we provide a novel perspective on GAN optimization, which helps us to consider whether a discriminator is metrizable. Definition 1.1 (Metrizable discriminator). Let µ θ and µ 0 be measures. Given an objective function J (θ; ·) for θ, a discriminator f is (J , D)or J -metrizable for µ θ and µ 0 , if J (θ; f ) is minimized only with θ ∈ arg min θ D(µ 0 , µ θ ) for a certain distance on measures, D(·, ·).To evaluate the dissimilarity with a given GAN minimization problem J , we are interested in other conditions besides the discriminator's optimality. Hence, we propose metrizable conditions, namely, direction optimality, separability, and injectivity, that induce J -metrizable discriminator. To achieve this, we first introduce a divergence, called functional mean divergence (FM * ), in Sec. 3. We connect the FM * with the minimization objective function of Wasserstein GAN. Then, we obtain the metrizable conditions for Wasserstein GAN by investigating Question 1.2. We provide an answer to this question in Sec. 4 by relating the FM * to the concept of sliced optimal transport(Bonneel et al., 2015;Kolouri et al., 2019). Then, in Sec. 5, we formalize the proposed conditions for Wasserstein GAN and further extend the result to generic GAN. Question 1.2. Under what conditions is FM * a distance?Based on the derived metrizable conditions, we propose the Slicing Adversarial Network (SAN) in Sec. 6. As seen inTable 1, we find that optimal discriminators for most existing GANs do not satisfy direction optimality. Hence, we develop a modification scheme for GAN maximization problems to enforce direction optimality on our discriminator. Owing to the scheme's simplicity, GANs can easily be converted to SANs. We conduct experiments to verify our perspective and demonstrate that SANs are superior to GANs in certain generation tasks on synthetic and image datasets. In particular, we confirmed a SAN improves state-of-the-art FID for conditional generation with StyleGAN-XL (Sauer et al., 2022) on ImageNet 256×256 despite the simple modifications. | SAN: INDUCING METRIZABILITY OF GAN WITH DISCRIMINATIVE NORMALIZED LINEAR LAYER |
d239016615 | Despite their success and popularity, deep neural networks (DNNs) are vulnerable when facing backdoor attacks. This impedes their wider adoption, especially in mission critical applications. This paper tackles the problem of Trojan detection, namely, identifying Trojaned models -models trained with poisoned data. One popular approach is reverse engineering, i.e., recovering the triggers on a clean image by manipulating the model's prediction. One major challenge of reverse engineering approach is the enormous search space of triggers. To this end, we propose innovative priors such as diversity and topological simplicity to not only increase the chances of finding the appropriate triggers but also improve the quality of the found triggers. Moreover, by encouraging a diverse set of trigger candidates, our method can perform effectively in cases with unknown target labels. We demonstrate that these priors can significantly improve the quality of the recovered triggers, resulting in substantially improved Trojan detection accuracy as validated on both synthetic and publicly available TrojAI benchmarks. | TRIGGER HUNTING WITH A TOPOLOGICAL PRIOR FOR TROJAN DETECTION |
d238419227 | We study the memorization power of feedforward ReLU neural networks. We show that such networks can memorize any N points that satisfy a mild separability assumption usingÕ √ N parameters. Known VC-dimension upper bounds imply that memorizing N samples requires Ω( √ N ) parameters, and hence our construction is optimal up to logarithmic factors. We also give a generalized construction for networks with depth bounded by 1 ≤ L ≤ √ N , for memorizing N samples usingÕ(N/L) parameters. This bound is also optimal up to logarithmic factors. Our construction uses weights with large bit complexity. We prove that having such a large bit complexity is both necessary and sufficient for memorization with a sub-linear number of parameters. * equal contribution | On the Optimal Memorization Power of ReLU Neural Networks |
d237491529 | Discrete-continuous hybrid action space is a natural setting in many practical problems, such as robot control and game AI. However, most previous Reinforcement Learning (RL) works only demonstrate the success in controlling with either discrete or continuous action space, while seldom take into account the hybrid action space. One naive way to address hybrid action RL is to convert the hybrid action space into a unified homogeneous action space by discretization or continualization, so that conventional RL algorithms can be applied. However, this ignores the underlying structure of hybrid action space and also induces the scalability issue and additional approximation difficulties, thus leading to degenerated results. In this paper, we propose Hybrid Action Representation (HyAR) to learn a compact and decodable latent representation space for the original hybrid action space. HyAR constructs the latent space and embeds the dependence between discrete action and continuous parameter via an embedding table and conditional Variational Auto-Encoder (VAE). To further improve the effectiveness, the action representation is trained to be semantically smooth through unsupervised environmental dynamics prediction. Finally, the agent then learns its policy with conventional DRL algorithms in the learned representation space and interacts with the environment by decoding the hybrid action embeddings to the original action space. We evaluate HyAR in a variety of environments with discrete-continuous action space. The results demonstrate the superiority of HyAR when compared with previous baselines, especially for high-dimensional action spaces.Published as a conference paper at ICLR 2022 that discretizing continuous parameter space suffers from the scalability issue due to the exponentially exploring number of discretized actions; while casting all discrete actions into a continuous dimension produces a piecewise-function action subspace, resulting in additional difficulties in approximation and generalization. To overcome these problems, a few recent works propose specific policy structures to learn DRL policies over the original hybrid action space directly. Parameterized Action DDPG (PADDPG) (Hausknecht & Stone, 2016) makes use of a DDPG(Lillicrap et al., 2015)structure where the actor is modified to output a unified continuous vector as the concatenation of values for all discrete actions and all corresponding continuous parameters. By contrast, Hybrid PPO (HPPO) (Fan et al., 2019) uses multiple policy heads consisting of one for discrete actions and the others for corresponding continuous parameter of each discrete action separately. A very similar idea is also adopted in(Peng & Tsuruoka, 2019). These methods are convenient to implement and are demonstrated to effective in simple environments with low-dimensional hybrid action space. However, PADDPG and HPPO neglect the dependence between discrete and continuous components of hybrid actions, thus can be problematic since the dependence is vital to identifying the optimal hybrid actions in general. Besides, the modeling of all continuous parameter dimensions all the time introduces redundancy in computation and policy learning, and may also have the scalability issue when the hybrid action space becomes high-dimensional. Algorithm Scalability Stationarity Dependence Latent PADDPG HPPO PDQN HHQN HyAR (Ours) Latent Space | HYAR: ADDRESSING DISCRETE-CONTINUOUS ACTION REINFORCEMENT LEARNING VIA HYBRID ACTION REPRESENTATION |
d262825257 | Given a sample of size N , it is often useful to select a subsample of smaller size n < N to be used for statistical estimation or learning. Such a data selection step is useful to reduce the requirements of data labeling and the computational complexity of learning. We assume to be given N unlabeled samples {x i } i≤N , and to be given access to a 'surrogate model' that can predict labels y i better than random guessing. Our goal is to select a subset of the samples, to be denoted by {x i } i∈G , of size |G| = n < N . We then acquire labels for this set and we use them to train a model via regularized empirical risk minimization.By using a mixture of numerical experiments on real and synthetic data, and mathematical derivations under low-and high-dimensional asymptotics, we show that: (i) Data selection can be very effective, in particular beating training on the full sample in some cases; (ii) Certain popular choices in data selection methods (e.g. unbiased reweighted subsampling, or influence function-based subsampling) can be substantially suboptimal. | Towards a statistical theory of data selection under weak supervision |
d259298630 | Overconfidence is a common issue for deep neural networks, limiting their deployment in real-world applications. To better estimate confidence, existing methods mostly focus on fully-supervised scenarios and rely on training labels. In this paper, we propose the first confidence estimation method for a semi-supervised setting, when most training labels are unavailable. We stipulate that even with limited training labels, we can still reasonably approximate the confidence of model on unlabeled samples by inspecting the prediction consistency through the training process. We use training consistency as a surrogate function and propose a consistency ranking loss for confidence estimation. On both image classification and segmentation tasks, our method achieves state-of-the-art performances in confidence estimation. Furthermore, we show the benefit of the proposed method through a downstream active learning task.Published as a conference paper at ICLR 2023 dence learning. For data without labels, our idea is to use the consistency of the predictions through the training process. An initial investigation suggests that consistency of predictions tends to be correlated with sample confidence on both labeled and unlabeled data.Having established training consistency as an approximation of confidence, the next challenge is that the consistency can only be evaluated on data available during training. To this end, we propose to re-calibrate model's prediction by aligning it with the consistency. In particular, we propose a novel Consistency Ranking Loss to regulate the model's output after the softmax layer so it has a similar ranking of model confidence output as the ranking of the consistency. After the re-calibration, we expect the model's output correctly accounts for its confidence on test samples. We both theoretically and empirically validate the effectiveness of the proposed Consistency Ranking Loss. Specifically, we show the superiority of our method on real applications, such as image classification and medical image segmentation, under semi-supervised settings. We also demonstrate the benefit of our method through active learning tasks. | CONFIDENCE ESTIMATION USING UNLABELED DATA |
d2971655 | Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent's parameters, which can lead to more consistent exploration and a richer set of behaviors. Methods such as evolutionary strategies use parameter perturbations, but discard all temporal structure in the process and require significantly more samples. Combining parameter noise with traditional RL methods allows to combine the best of both worlds. We demonstrate that both off-and on-policy methods benefit from this approach through experimental comparison of DQN, DDPG, and TRPO on high-dimensional discrete action environments as well as continuous control tasks. Our results show that RL with parameter noise learns more efficiently than traditional RL with action space noise and evolutionary strategies individually. | Parameter Space Noise for Exploration |
d221655767 | Visual cognition of primates is superior to that of artificial neural networks in its ability to "envision" a visual object, even a newly-introduced one, in different attributes including pose, position, color, texture, etc. To aid neural networks to envision objects with different attributes, we propose a family of objective functions, expressed on groups of examples, as a novel learning framework that we term Group-Supervised Learning (GSL). GSL decomposes inputs into a disentangled representation with swappable components that can be recombined to synthesize new samples, trained through similarity mining within groups of exemplars. For instance, images of red boats & blue cars can be decomposed and recombined to synthesize novel images of red cars. We describe a general class of datasets admissible by GSL. We propose an implementation based on auto-encoder, termed group-supervised zero-shot synthesis network (GZS-Net) trained with our learning framework, that can produce a high-quality red car even if no such example is witnessed during training. We test our model and learning framework on existing benchmarks, in addition to new dataset that we open-source. We qualitatively and quantitatively demonstrate that GZS-Net trained with GSL outperforms state-of-the-art methods. | Zero-shot Synthesis with Group-Supervised Learning |
d251622295 | Multi-objective reinforcement learning (MORL) approaches have emerged to tackle many real-world problems with multiple conflicting objectives by maximizing a joint objective function weighted by a preference vector. These approaches find fixed customized policies corresponding to preference vectors specified during training. However, the design constraints and objectives typically change dynamically in real-life scenarios. Furthermore, storing a policy for each potential preference is not scalable. Hence, obtaining a set of Pareto front solutions for the entire preference space in a given domain with a single training is critical. To this end, we propose a novel MORL algorithm that trains a single universal network to cover the entire preference space scalable to continuous robotic tasks. The proposed approach, Preference-Driven MORL (PD-MORL), utilizes the preferences as guidance to update the network parameters. It also employs a novel parallelization approach to increase sample efficiency. We show that PD-MORL achieves up to 25% larger hypervolume for challenging continuous control tasks and uses an order of magnitude fewer trainable parameters compared to prior approaches.arXiv:2208.07914v3 [cs.LG] 29 May 2023Published as a conference paper at ICLR 2023 preference-driven (PD) MORL. For each episode during training, we randomly sample a preference vector (ω ∈ Ω : L i=0 ω i = 1) from a uniform distribution. Since the number of collected transitions by interacting with the environment for some preferences may be underrepresented, we utilize hindsight experience replay buffer (HER)(Andrychowicz et al., 2017). As a key insight, we observe that the preference vectors have similar directional angles to the corresponding vectorized Q-values for a given state. Using the insight, we utilize the cosine similarity between the preference vector and vectorized Q-values in the Bellman's optimality operator to guide the training. However, not every Pareto front perfectly aligned with the preference vectors. To mitigate this adverse effect, we fit a multi-dimensional interpolator to project the original preference vectors (ω ∈ Ω) to normalized solution space to align preferences with the multi-objective solutions. The projected preference vectors are used in our novel preference-driven optimality operator to obtain the target Q-values. Additionally, to increase the sample efficiency of the algorithm, we divide the preference space into sub-spaces and assign a child process to these sub-spaces. Each child process is responsible for its own preference sub-space to collect transitions. This parallelization provides efficient exploration during training, assuring that there is no bias towards any preference sub-space. | PD-MORL: PREFERENCE-DRIVEN MULTI-OBJECTIVE REINFORCEMENT LEARNING ALGORITHM |
d108297336 | Presently the most successful approaches to semi-supervised learning are based on consistency regularization, whereby a model is trained to be robust to small perturbations of its inputs and parameters. To understand consistency regularization, we conceptually explore how loss geometry interacts with training procedures. The consistency loss dramatically improves generalization performance over supervisedonly training; however, we show that SGD struggles to converge on the consistency loss and continues to make large steps that lead to changes in predictions on the test data. Motivated by these observations, we propose to train consistencybased methods with Stochastic Weight Averaging (SWA), a recent approach which averages weights along the trajectory of SGD with a modified learning rate schedule. We also propose fast-SWA, which further accelerates convergence by averaging multiple points within each cycle of a cyclical learning rate schedule. With weight averaging, we achieve the best known semi-supervised results on CIFAR-10 and CIFAR-100, over many different quantities of labeled training data. For example, we achieve 5.0% error on CIFAR-10 with only 4000 labels, compared to the previous best result in the literature of 6.3%.Izmailov et al., 2018). Interpolating between the weights corresponding to different epochs of training we demonstrate that the solutions of Π and Mean Teacher models are indeed flatter along these directions(Figure 1b). | THERE ARE MANY CONSISTENT EXPLANATIONS OF UNLABELED DATA: WHY YOU SHOULD AVERAGE |
d3461223 | Domain adaptation refers to the problem of leveraging labeled data in a source domain to learn an accurate model in a target domain where labels are scarce or unavailable. A recent approach for finding a common representation of the two domains is via domain adversarial training(Ganin & Lempitsky, 2015), which attempts to induce a feature extractor that matches the source and target feature distributions in some feature space. However, domain adversarial training faces two critical limitations: 1) if the feature extraction function has high-capacity, then feature distribution matching is a weak constraint, 2) in non-conservative domain adaptation (where no single classifier can perform well in both the source and target domains), training the model to do well on the source domain hurts performance on the target domain. In this paper, we address these issues through the lens of the cluster assumption, i.e., decision boundaries should not cross high-density data regions. We propose two novel and related models: 1) the Virtual Adversarial Domain Adaptation (VADA) model, which combines domain adversarial training with a penalty term that punishes violation of the cluster assumption; 2) the Decision-boundary Iterative Refinement Training with a Teacher (DIRT-T) 1 model, which takes the VADA model as initialization and employs natural gradient steps to further minimize the cluster assumption violation. Extensive empirical results demonstrate that the combination of these two models significantly improve the state-of-the-art performance on the digit, traffic sign, and Wi-Fi recognition domain adaptation benchmarks. | A DIRT-T APPROACH TO UNSUPERVISED DOMAIN ADAPTATION |
d203736888 | We present a novel generative model that combines state-of-the-art neural text-tospeech (TTS) with semi-supervised probabilistic latent variable models. By providing partial supervision to some of the latent variables, we are able to force them to take on consistent and interpretable purposes, which previously hasn't been possible with purely unsupervised TTS models. We demonstrate that our model is able to reliably discover and control important but rarely labelled attributes of speech, such as affect and speaking rate, with as little as 1% (30 minutes) supervision. Even at such low supervision levels we do not observe a degradation of synthesis quality compared to a state-of-the-art baseline. Audio samples are available on the web 1 . | SEMI-SUPERVISED GENERATIVE MODELING FOR CONTROLLABLE SPEECH SYNTHESIS |
d264426013 | In distributed deep learning with data parallelism, synchronizing gradients at each training step can cause a huge communication overhead, especially when many nodes work together to train large models.Local gradient methods, such as Local SGD, address this issue by allowing workers to compute locally for H steps without synchronizing with others, hence reducing communication frequency.While H has been viewed as a hyperparameter to trade optimization efficiency for communication cost, recent research indicates that setting a proper H value can lead to generalization improvement.Yet, selecting a proper H is elusive.This work proposes a theory-grounded method for determining H, named the Quadratic Synchronization Rule (QSR), which recommends dynamically setting H in proportion to 1 η 2 as the learning rate η decays over time.Extensive ImageNet experiments on ResNet and ViT show that local gradient methods with QSR consistently improve the test accuracy over other synchronization strategies. 1 Compared with the standard data parallel training, QSR enables Local AdamW on ViT-B to cut the training time on 16 or 64 GPUs down from 26.7 to 20.2 hours or from 8.6 to 5.5 hours and, at the same time, achieves 1.16% or 0.84% higher top-1 validation accuracy. | A QUADRATIC SYNCHRONIZATION RULE FOR DISTRIBUTED DEEP LEARNING |
d268041608 | Accurate blind docking has the potential to lead to new biological breakthroughs, but for this promise to be realized, docking methods must generalize well across the proteome.Existing benchmarks, however, fail to rigorously assess generalizability.Therefore, we develop DOCKGEN, a new benchmark based on the ligandbinding domains of proteins, and we show that existing machine learning-based docking models have very weak generalization abilities.We carefully analyze the scaling laws of ML-based docking and show that, by scaling data and model size, as well as integrating synthetic data strategies, we are able to significantly increase the generalization capacity and set new state-of-the-art performance across benchmarks.Further, we propose CONFIDENCE BOOTSTRAPPING, a new training paradigm that solely relies on the interaction between diffusion and confidence models and exploits the multi-resolution generation process of diffusion models.We demonstrate that CONFIDENCE BOOTSTRAPPING significantly improves the ability of ML-based docking methods to dock to unseen protein classes, edging closer to accurate and generalizable blind docking methods. | DEEP CONFIDENT STEPS TO NEW POCKETS: STRATEGIES FOR DOCKING GENERALIZATION |
d257404922 | Premise selection is a fundamental problem of automated theorem proving. Previous works often use intricate symbolic methods, rely on domain knowledge, and require significant engineering effort to solve this task. In this work, we show that Magnushammer, a neural transformer-based approach, can outperform traditional symbolic systems by a large margin. Tested on the PISA benchmark, Magnushammer achieves 59.5% proof rate compared to a 38.3% proof rate of Sledgehammer, the most mature and popular symbolic-based solver. Furthermore, by combining Magnushammer with a neural formal prover based on a language model, we significantly improve the previous state-of-the-art proof rate from 57.0% to 71.0%. | Magnushammer: A Transformer-based Approach to Premise Selection |
d263792492 | Text-guided 3D shape generation remains challenging due to the absence of large paired text-shape dataset, the substantial semantic gap between these two modalities, and the structural complexity of 3D shapes. This paper presents a new framework called Image as Stepping Stone (ISS) for the task by introducing 2D image as a stepping stone to connect the two modalities and to eliminate the need for paired text-shape data. Our key contribution is a two-stage feature-space-alignment approach that maps CLIP features to shapes by harnessing a pre-trained single-view reconstruction (SVR) model with multi-view supervisions: first map the CLIP image feature to the detail-rich shape space in the SVR model, then map the CLIP text feature to the shape space and optimize the mapping by encouraging CLIP consistency between the input text and the rendered images. Further, we formulate a textguided shape stylization module to dress up the output shapes with novel structures and textures. Beyond existing works on 3D shape generation from text, our new approach is general for creating shapes in a broad range of categories, without requiring paired text-shape data. Experimental results manifest that our approach outperforms the state-of-the-arts and our baselines in terms of fidelity and consistency with text. Further, our approach can stylize the generated shapes with both realistic and fantasy structures and textures. Codes are available at https://github.com/ liuzhengzhe/ISS-Image-as-Stepping-Stone-for-Text-Guided-3D-Shape-Generation.A green bedside lamp.Figure 1: Our novel "Image as Stepping Stone" framework (a) is able to connect the text space (the CLIP Text feature) and the 3D shape space (the SVR feature) through our two-stage feature-space alignment, such that we can generate plausible 3D shapes from text (b) beyond the capabilities of the existing works (CLIP-Forge and Dream Fields), without requiring paired text-shape data.Published as a conference paper at ICLR 2023 Very recently, two annotation-free approaches, CLIP-Forge (Sanghi et al., 2022) and Dream Fields (Jain et al., 2022), were proposed to address the dataset limitation. These two state-ofthe-art approaches attempt to utilize the joint text-image embedding from the large-scale pre-trained language vision model, i.e., CLIP (Radford et al., 2021), to eliminate the need of requiring paired text-shape data in model training. However, it is still extremely challenging to generate 3D shapes from text without paired texts and shapes for the following reasons. First, the range of object categories that can be generated are still limited due to the scarcity of 3D datasets. For example, Clip-Forge (Sanghi et al., 2022) is built upon a shape auto-encoder; it can hardly generate plausible shapes beyond the ShapeNet categories. Also, it is challenging to learn 3D prior of the desired shape from texts. For instance, Dream Field (Jain et al., 2022) cannot generate 3D shapes like our approach due to the lack of 3D prior, as it is trained to produce only multi-view images with a neural radiance field. Further, with over an hour of optimization for each shape instance from scratch, there is still no guarantee that the multi-view consistency constraint of Dream Field (Jain et al., 2022) can enforce the model for producing shapes that match the given text; we will provide further investigation in our experiments. Last, the visual quality of the generated shapes is far from satisfactory due to the substantial semantic gap between the unpaired texts and shapes. As shown inFigure 1 (b), the results generated by Dream Field typically look surrealistic (rather than real), due to insufficient information extracted from text for the shape structures and details. On the other hand, CLIP-Forge (Sanghi et al., 2022) is highly restricted by the limited 64 3 resolution and it lacks colors and textures, further manifesting the difficulty of generating 3D shapes from unpaired text-shape data.Going beyond the existing works, we propose a new approach to 3D shape generation from text without needing paired text-shape data. Our key idea is to implicitly leverage 2D image as a stepping stone (ISS) to connect the text and shape modalities. Specifically, we employ the joint text-image embedding in CLIP and train a CLIP2Shape mapper to map the CLIP image features to a pre-trained detail-rich 3D shape space with multi-view supervisions; seeFigure 1(a): stage 1. Thanks to the joint text-image embedding from CLIP, our trained mapper is able to connect the CLIP text features with the shape space for text-guided 3D shape generation. Yet, due to the gap between the CLIP text and CLIP image features, the mapped text feature may not align well with the destination shape feature; see the empirical analysis in Section 3.2. Hence, we further fine-tune the mapper specific to each text input by encouraging CLIP consistency between the rendered images and the input text to enhance the consistency between the input text and the generated shape; seeFigure 1(a): stage 2.Our new approach advances the frontier of 3D shape generation from text in the following aspects. First, by taking image as a stepping stone, we make the challenging text-guided 3D shape generation task more approachable and cast it as a single-view reconstruction (SVR) task. Having said that, we learn 3D shape priors from the adopted SVR model directly in the feature space. Second, benefiting from the learned 3D priors from the SVR model and the joint text-image embeddings, our approach can produce 3D shapes in only 85 seconds vs. 72 minutes of Dream Fields (Jain et al., 2022). More importantly, our approach is able to produce plausible 3D shapes, not multi-view images, beyond the generation capabilities of the state-of-the-art approaches; seeFigure 1(b).With our two-stage feature-space alignment, we already can generate shapes with good fidelity from texts. To further enrich the generated shapes with vivid textures and structures beyond the generative space of the pre-trained SVR model, we additionally design a text-guided stylization module to generate novel textures and shapes by encouraging consistency between the rendered images and the text description of the target style. We then can effectively fuse with the two-stage feature-space alignment to enable the generation of both realistic and fantasy textures and also shapes beyond the generation capability of the SVR model; seeFigure 1(b) for examples. Furthermore, our approach is compatible with different SVR models(Niemeyer et al., 2020;Alwala et al., 2022). For example, we can adopt SS3D (Alwala et al., 2022) to generate shapes from single-view in-the-wild images to broaden the range of categorical 3D shapes that our approach can generate, going beyond Sanghi et al. (2022), which can only generate 13 categories of ShapeNet. Besides, our approach can also work with the very recent approach GET3D (Gao et al., 2022) to generate high-quality 3D shapes from text; see our results in Section 10.RELATED WORKSText-guided image generation. Existing text-guided image generation approaches can be roughly cast into two branches: (i) direct image synthesis(Reed et al., 2016a;b;Zhang et al., 2017; | ISS: IMAGE AS STEPPING STONE FOR TEXT-GUIDED 3D SHAPE GENERATION |
d247292326 | Federated learning (FL) enables distributed optimization of machine learning models while protecting privacy by independently training local models on each client and then aggregating parameters on a central server, thereby producing an effective global model. Although a variety of FL algorithms have been proposed, their training efficiency remains low when the data are not independently and identically distributed (non-i.i.d.) across different clients. We observe that the slow convergence rates of the existing methods are (at least partially) caused by the catastrophic forgetting issue during the local training stage on each individual client, which leads to a large increase in the loss function concerning the previous training data at the other clients. Here, we propose FedReg, an algorithm to accelerate FL with alleviated knowledge forgetting in the local training stage by regularizing locally trained parameters with the loss on generated pseudo data, which encode the knowledge of previous training data learned by the global model. Our comprehensive experiments demonstrate that FedReg not only significantly improves the convergence rate of FL, especially when the neural network architecture is deep and the clients' data are extremely non-i.i.d., but is also able to protect privacy better in classification problems and more robust against gradient inversion attacks. The code is available at: https://github.com/Zoesgithub/FedReg. * Minlie Huang and Tao Jiang are the co-corresponding authors. | ACCELERATION OF FEDERATED LEARNING WITH ALLEVIATED FORGETTING IN LOCAL TRAINING |
d15534684 | Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification. For instance, a good representation for 2D images might be one that describes only global structure and discards information about detailed texture. In this paper, we present a simple but principled method to learn such global representations by combining Variational Autoencoder (VAE) with neural autoregressive models such as RNN, MADE and PixelRNN/CNN. Our proposed VAE model allows us to have control over what the global latent code can learn and by designing the architecture accordingly, we can force the global latent code to discard irrelevant information such as texture in 2D images, and hence the VAE only "autoencodes" data in a lossy fashion. In addition, by leveraging autoregressive models as both prior distribution p(z) and decoding distribution p(x|z), we can greatly improve generative modeling performance of VAEs, achieving new state-of-the-art results on MNIST, OMNIGLOT and Caltech-101 Silhouettes density estimation tasks as well as competitive results on CIFAR10. | VARIATIONAL LOSSY AUTOENCODER |
d245329426 | Reinforcement learning (RL) provides a naturalistic framing for learning through trial and error, which is appealing both because of its simplicity and effectiveness and because of its resemblance to how humans and animals acquire skills through experience. However, real-world embodied learning, such as that performed by humans and animals, is situated in a continual, non-episodic world, whereas common benchmark tasks in RL are episodic, with the environment resetting between trials to provide the agent with multiple attempts. This discrepancy presents a major challenge when attempting to take RL algorithms developed for episodic simulated environments and run them on real-world platforms, such as robots.In this paper, we aim to address this discrepancy by laying out a framework for Autonomous Reinforcement Learning (ARL): reinforcement learning where the agent not only learns through its own experience, but also contends with lack of human supervision to reset between trials. We introduce a simulated benchmark EARL 1 around this framework, containing a set of diverse and challenging simulated tasks reflective of the hurdles introduced to learning when only a minimal reliance on extrinsic intervention can be assumed. We show that standard approaches to episodic RL and existing approaches struggle as interventions are minimized, underscoring the need for developing new algorithms for reinforcement learning with a greater focus on autonomy. | AUTONOMOUS REINFORCEMENT LEARNING: FORMALISM AND BENCHMARKING |
d222134104 | Natural data are often long-tail distributed over semantic classes. Existing recognition methods tackle this imbalanced classification by placing more emphasis on the tail data, through class re-balancing/re-weighting or ensembling over different data groups, resulting in increased tail accuracies but reduced head accuracies. We take a dynamic view of the training data and provide a principled model bias and variance analysis as the training data fluctuates: Existing long-tail classifiers invariably increase the model variance and the head-tail model bias gap remains large, due to more and larger confusion with hard negatives for the tail. We propose a new long-tailed classifier called RoutIng Diverse Experts (RIDE). It reduces the model variance with multiple experts, reduces the model bias with a distribution-aware diversity loss, reduces the computational cost with a dynamic expert routing module. RIDE outperforms the state-of-the-art by 5% to 7% on CIFAR100-LT, ImageNet-LT and iNaturalist 2018 benchmarks. It is also a universal framework that is applicable to various backbone networks, such as ResNet, ResNeXt and Swin Transformer, long-tailed algorithms, and training mechanisms for consistent performance gains. Our code is available at: https://github.com/frank-xwang/RIDE-LongTailRecognition. arXiv:2010.01809v4 [cs.CV] 1 May 2022 Published as a conference paper at ICLR 2021 Under review as a conference paper at ICLR 2021 All Many-shot Med-shot Few-shot acc bias var acc bias var acc bias var acc bias var CE 31.6 0.60 0.47 57.3 0.28 0.35 28.2 0.61 0.51 6.3 0.94 0.57 ⌧ -norm 35.8 0.52 0.49 55.9 0.28 0.37 33.2 0.53 0.52 16.1 0.78 0.60 cRT 36.4 0.50 0.50 51.3 0.32 0.41 38.6 0.44 0.50 17.0 0.76 0.61 LDAM 34.4 0.53 0.51 55.1 0.28 0.38 31.9 0.53 0.54 13.9 0.81 0.63 RIDE + LDAM 40.5 0.50 0.42 60.5 0.28 0.30 38.7 0.50 0.44 20.1 0.74 0.52ABSTRACT Natural data are often long-tail distributed over semantic classes. Existing recognition methods tend to focus on tail performance gain, often at the expense of head performance loss from increased classifier variance. The low tail performance manifests itself in large between-class confusion and high classifier variance. We aim to reduce both the bias and the variance of a long-tailed classifier by RoutIng Diverse Experts (RIDE). It has three components: 1) a shared architecture for multiple classifiers (experts); 2) a distribution-aware diversity loss that encourages more diverse decisions for classes with fewer training instances; and 3) an expert routing module that dynamically assigns more ambiguous instances to additional experts. With on-par computational complexity, RIDE significantly outperforms the state-of-the-art methods by 5% to 7% on all the benchmarks including CIFAR100-LT, ImageNet-LT and iNaturalist. RIDE is also a universal framework that can be applied to different backbone networks and integrated into various re-balancing or re-weighting methods for consistent performance gains. | LONG-TAILED RECOGNITION BY ROUTING DIVERSE DISTRIBUTION-AWARE EXPERTS |
d219721074 | Despite their popularity, to date, the application of normalizing flows on categorical data stays limited. The current practice of using dequantization to map discrete data to a continuous space is inapplicable as categorical data has no intrinsic order. Instead, categorical data have complex and latent relations that must be inferred, like the synonymy between words. In this paper, we investigate Categorical Normalizing Flows, that is normalizing flows for categorical data. By casting the encoding of categorical data in continuous space as a variational inference problem, we jointly optimize the continuous representation and the model likelihood. To maintain unique decoding, we learn a partitioning of the latent space by factorizing the posterior. Meanwhile, the complex relations between the categorical variables are learned by the ensuing normalizing flow, thus maintaining a close-to exact likelihood estimate and making it possible to scale up to a large number of categories. Based on Categorical Normalizing Flows, we propose GraphCNF a permutation-invariant generative model on graphs, outperforming both one-shot and autoregressive flow-based state-of-the-art on molecule generation. Preprint. Under review. arXiv:2006.09790v1 [cs.LG] 17 Jun 2020flow. As no information should be lost when mapping the data into continuous space, we limit the encoding distributions to ones whose (approximate) posterior is independent over discrete variables. This leads to a learned partitioning of the latent space with an almost unique decoding. We call this approach Categorical Normalizing Flows and experiment with encoding distributions of increasing flexibility, but find that a simple mixture model is sufficient for encoding categorical data well.Categorical Normalizing Flows can be applied to any task involving categorical variables. Examples, which we visit experimentally in this work, include words as categorical (one-hot vector) variables, sets and graphs[48,50]. We put particular emphasis on graphs, as current approaches are mostly autoregressive[18,36,49]and view graphs as sequences, although there exists no intrinsic order of the nodes. Normalizing flows, however, can perform generation in parallel making a definition of order unnecessary. By treating both nodes and edges as categorical variables, we employ our variational inference encoding and propose GraphCNF. GraphCNF is a novel permutation-invariant normalizing flow on graph generation which assigns equal likelihood to any ordering of nodes. Meanwhile, GraphCNF encodes the node attributes, edge attributes and graph structure in three consecutive steps. As shown in the experiments, the improved encoding and flow architecture allows GraphCNF to outperform significantly both the autoregressive and parallel flow-based state-of-the-art.Overall, our contributions are summarized as follows:• We propose Categorical Normalizing Flows, which apply a novel encoding method for categorical data in normalizing flows. By using variational inference with a factorized posterior, we still support an close-to exact likelihood estimate and scale up to large number of categories. • Starting from the Categorical Normalizing Flows, we propose GraphCNF, a permutationinvariant normalizing flow on graph generation. On molecule generation, GraphCNF sets a new state-of-the-art for flow-based methods outperforming one-shot and autoregressive baselines. • We experiment with encoding distributions of increasing flexibility on various tasks including sets, language and graphs, and show that a simple mixture model is sufficient for modeling discrete, categorical distribution accurately. | Categorical Normalizing Flows via Continuous Transformations |
d235613568 | Federated learning aims to collaboratively train a strong global model by accessing users' locally trained models but not their own data. A crucial step is therefore to aggregate local models into a global model, which has been shown challenging when users have non-i.i.d. data. In this paper, we propose a novel aggregation algorithm named FEDBE, which takes a Bayesian inference perspective by sampling higher-quality global models and combining them via Bayesian model Ensemble, leading to much robust aggregation. We show that an effective model distribution can be constructed by simply fitting a Gaussian or Dirichlet distribution to the local models. Our empirical studies validate FEDBE's superior performance, especially when users' data are not i.i.d. and when the neural networks go deeper. Moreover, FEDBE is compatible with recent efforts in regularizing users' model training, making it an easily applicable module: you only need to replace the aggregation method but leave other parts of your federated learning algorithm intact. Our code is publicly available at https://github.com/hongyouc/FedBE. Eric Brochu, Vlad M Cora, and Nando De Freitas. A tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint arXiv: | FEDBE: MAKING BAYESIAN MODEL ENSEMBLE APPLICABLE TO FEDERATED LEARNING |
d210843644 | Message-passing neural networks (MPNNs) have been successfully applied to representation learning on graphs in a variety of real-world applications. However, two fundamental weaknesses of MPNNs' aggregators limit their ability to represent graph-structured data: losing the structural information of nodes in neighborhoods and lacking the ability to capture long-range dependencies in disassortative graphs. Few studies have noticed the weaknesses from different perspectives. From the observations on classical neural network and network geometry, we propose a novel geometric aggregation scheme for graph neural networks to overcome the two weaknesses. The behind basic idea is the aggregation on a graph can benefit from a continuous space underlying the graph. The proposed aggregation scheme is permutation-invariant and consists of three modules, node embedding, structural neighborhood, and bi-level aggregation. We also present an implementation of the scheme in graph convolutional networks, termed Geom-GCN, to perform transductive learning on graphs. Experimental results show the proposed Geom-GCN achieved state-of-the-art performance on a wide range of open datasets of graphs. | GEOM-GCN: GEOMETRIC GRAPH CONVOLUTIONAL NETWORKS |
d227126808 | In this work, we examine the security of InstaHide, a scheme recently proposed by [Huang, Song, Li and Arora, ICML'20] for preserving the security of private datasets in the context of distributed learning. To generate a synthetic training example to be shared among the distributed learners, InstaHide takes a convex combination of private feature vectors and randomly flips the sign of each entry of the resulting vector with probability 1/2. A salient question is whether this scheme is secure in any provable sense, perhaps under a plausible hardness assumption and assuming the distributions generating the public and private data satisfy certain properties.We show that the answer to this appears to be quite subtle and closely related to the average-case complexity of a new multi-task, missing-data version of the classic problem of phase retrieval. Motivated by this connection, we design a provable algorithm that can recover private vectors using only the public vectors and synthetic vectors generated by InstaHide, under the assumption that the private and public vectors are isotropic Gaussian. * sitanc@mit.edu. | On InstaHide, Phase Retrieval, and Sparse Matrix Factorization |
d49667762 | Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train. Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as machine translation, with the added advantage that they concurrently process all inputs in the sequence, leading to easy parallelization and faster training times. Despite these successes, however, popular feed-forward sequence models like the Transformer fail to generalize in many simple tasks that recurrent models handle with ease, e.g. copying strings or even simple logical inference when the string or formula lengths exceed those observed at training time. We propose the Universal Transformer (UT), a parallel-in-time self-attentive recurrent sequence model which can be cast as a generalization of the Transformer model and which addresses these issues. UTs combine the parallelizability and global receptive field of feed-forward sequence models like the Transformer with the recurrent inductive bias of RNNs. We also add a dynamic per-position halting mechanism and find that it improves accuracy on several tasks. In contrast to the standard Transformer, under certain assumptions UTs can be shown to be Turing-complete. Our experiments show that UTs outperform standard Transformers on a wide range of algorithmic and language understanding tasks, including the challenging LAMBADA language modeling task where UTs achieve a new state of the art, and machine translation where UTs achieve a 0.9 BLEU improvement over Transformers on the WMT14 En-De dataset. * Equal contribution, alphabetically by last name. † Work performed while at Google Brain. | UNIVERSAL TRANSFORMERS |
d259308864 | We study the problem of watermarking large language models (LLMs) generated text -one of the most promising approaches for addressing the safety challenges of LLM usage. In this paper, we propose a rigorous theoretical framework to quantify the effectiveness and robustness of LLM watermarks. We propose a robust and high-quality watermark method, Unigram-Watermark, by extending an existing approach with a simplified fixed grouping strategy. We prove that our watermark method enjoys guaranteed generation quality, correctness in watermark detection, and is robust against text editing and paraphrasing. Experiments on three varying LLMs and two datasets verify that our Unigram-Watermark achieves superior detection accuracy and comparable generation quality in perplexity, thus promoting the responsible use of LLMs. Code is available at https://github.com/XuandongZhao/Unigram-Watermark. | Provable Robust Watermarking for AI-Generated Text |
d44084312 | A We characterize the singular values of the linear transformation associated with a standard 2D multi-channel convolutional layer, enabling their efficient computation. This characterization also leads to an algorithm for projecting a convolutional layer onto an operator-norm ball. We show that this is an effective regularizer; for example, it improves the test error of a deep residual network using batch normalization on CIFAR-10 from 6.2% to 5.3%. | The Singular Values of Convolutional Layers |
d262411072 | We present an algorithm for policy search in stochastic dynamical systems using model-based reinforcement learning.The system dynamics are described with Bayesian neural networks (BNNs) that include stochastic input variables.These input variables allow us to capture complex statistical patterns in the transition dynamics (e.g.multi-modality and heteroskedasticity), which are usually missed by alternative modeling approaches.After learning the dynamics, our BNNs are then fed into an algorithm that performs random roll-outs and uses stochastic optimization for policy learning.We train our BNNs by minimizing α-divergences with α = 0.5, which usually produces better results than other techniques such as variational Bayes.We illustrate the performance of our method by solving a challenging problem where model-based approaches usually fail and by obtaining promising results in real-world scenarios including the control of a gas turbine and an industrial benchmark. | LEARNING AND POLICY SEARCH IN STOCHASTIC DYNAMICAL SYSTEMS WITH BAYESIAN NEURAL NETWORKS |
d220249831 | Real-world large-scale datasets are heteroskedastic and imbalanced -labels have varying levels of uncertainty and label distributions are long-tailed. Heteroskedasticity and imbalance challenge deep learning algorithms due to the difficulty of distinguishing among mislabeled, ambiguous, and rare examples. Addressing heteroskedasticity and imbalance simultaneously is under-explored. We propose a data-dependent regularization technique for heteroskedastic datasets that regularizes different regions of the input space differently. Inspired by the theoretical derivation of the optimal regularization strength in a one-dimensional nonparametric classification setting, our approach adaptively regularizes the data points in higher-uncertainty, lower-density regions more heavily. We test our method on several benchmark tasks, including a real-world heteroskedastic and imbalanced dataset, WebVision. Our experiments corroborate our theory and demonstrate a significant improvement over other methods in noise-robust deep learning. | Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization |
d244130255 | Despite the recent success of Graph Neural Networks (GNNs), training GNNs on large graphs remains challenging. The limited resource capacities of the existing servers, the dependency between nodes in a graph, and the privacy concern due to the centralized storage and model learning have spurred the need to design an effective distributed algorithm for GNN training. However, existing distributed GNN training methods impose either excessive communication costs or large memory overheads that hinders their scalability. To overcome these issues, we propose a communication-efficient distributed GNN training technique named Learn Locally, Correct Globally (LLCG). To reduce the communication and memory overhead, each local machine in LLCG first trains a GNN on its local data by ignoring the dependency between nodes among different machines, then sends the locally trained model to the server for periodic model averaging. However, ignoring node dependency could result in significant performance degradation. To solve the performance degradation, we propose to apply Global Server Corrections on the server to refine the locally learned models. We rigorously analyze the convergence of distributed methods with periodic model averaging for training GNNs and show that naively applying periodic model averaging but ignoring the dependency between nodes will suffer from an irreducible residual error. However, this residual error can be eliminated by utilizing the proposed global corrections to entail fast convergence rate. Extensive experiments on real-world datasets show that LLCG can significantly improve the efficiency without hurting the performance. | LEARN LOCALLY, CORRECT GLOBALLY: A DISTRIBUTED ALGORITHM FOR TRAINING GRAPH NEURAL NETWORKS |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.