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d235376822 | We extend semi-supervised learning to the problem of domain adaptation to learn significantly higher-accuracy models that train on one data distribution and test on a different one. With the goal of generality, we introduce AdaMatch, a unified solution for unsupervised domain adaptation (UDA), semi-supervised learning (SSL), and semi-supervised domain adaptation (SSDA). In an extensive experimental study, we compare its behavior with respective state-of-the-art techniques from SSL, SSDA, and UDA and find that AdaMatch either matches or significantly exceeds the state-of-the-art in each case using the same hyper-parameters regardless of the dataset or task. For example, AdaMatch nearly doubles the accuracy compared to that of the prior state-of-the-art on the UDA task for DomainNet and even exceeds the accuracy of the prior state-of-the-art obtained with pre-training by 6.4% when AdaMatch is trained completely from scratch. Furthermore, by providing AdaMatch with just one labeled example per class from the target domain (i.e., the SSDA setting), we increase the target accuracy by an additional 6.1%, and with 5 labeled examples, by 13.6%. 1 * equal contribution 1 Code to reproduce results: | ADAMATCH: A UNIFIED APPROACH TO SEMI- SUPERVISED LEARNING AND DOMAIN ADAPTATION |
d248968007 | Self-driving cars must detect vehicles, pedestrians, and other traffic participants accurately to operate safely.Small, far-away, or highly occluded objects are particularly challenging because there is limited information in the LiDAR point clouds for detecting them.To address this challenge, we leverage valuable information from the past: in particular, data collected in past traversals of the same scene.We posit that these past data, which are typically discarded, provide rich contextual information for disambiguating the above-mentioned challenging cases.To this end, we propose a novel, end-to-end trainable HIND-SIGHT framework to extract this contextual information from past traversals and store it in an easy-to-query data structure, which can then be leveraged to aid future 3D object detection of the same scene.We show that this framework is compatible with most modern 3D detection architectures and can substantially improve their average precision on multiple autonomous driving datasets, most notably by more than 300% on the challenging cases.Our code is available at https://github.com/YurongYou/Hindsight. | |
d257623048 | There has been growing interest in automatically predicting missing type annotations in programs written in Python and JavaScript. While prior methods have achieved impressive accuracy when predicting the most common types, they often perform poorly on rare or complex types. In this paper, we present a new type inference method that treats type prediction as a code infilling task by leveraging CodeT5, a state-of-the-art seq2seq pre-trained language model for code. Our method uses static analysis to construct dynamic contexts for each code element whose type signature is to be predicted by the model. We also propose an iterative decoding scheme that incorporates previous type predictions in the model's input context, allowing information exchange between related code elements. Our evaluation shows that the proposed approach, TypeT5, not only achieves a higher overall accuracy (particularly on rare and complex types) but also produces more coherent results with fewer type errors-while enabling easy user intervention. | Published as a conference paper at ICLR 2023 TYPET5: SEQ2SEQ TYPE INFERENCE USING STATIC ANALYSIS |
d257767364 | Automatically optimizing the hyperparameters of Machine Learning algorithms is one of the primary open questions in AI. Existing work in Hyperparameter Optimization (HPO) trains surrogate models for approximating the response surface of hyperparameters as a regression task. In contrast, we hypothesize that the optimal strategy for training surrogates is to preserve the ranks of the performances of hyperparameter configurations as a Learning to Rank problem. As a result, we present a novel method that meta-learns neural network surrogates optimized for ranking the configurations' performances while modeling their uncertainty via ensembling. In a large-scale experimental protocol comprising 12 baselines, 16 HPO search spaces and 86 datasets/tasks, we demonstrate that our method achieves new state-of-the-art results in HPO. * Equal contribution 1 Our code is available in the following repository: https://github.com/releaunifreiburg/ DeepRankingEnsembles arXiv:2303.15212v2 [cs.LG] 21 May 2023Published as a conference paper at ICLR 2023 systematic treatment of HPO through a methodologically-principled L2R formulation. To achieve state-of-the-art HPO results, we follow the established practice of transfer-learning the ranking surrogates from evaluations on previous datasets . Furthermore, we boost the transfer quality by using dataset meta-features as an extra source of information(Jomaa et al., 2021a).We conducted large-scale experiments using HPO-B (Pineda Arango et al., 2021), the largest public HPO benchmark and compared them against 12 state-of-the-art HPO baselines. We ultimately demonstrate that our method Deep Ranking Ensembles (DRE) sets the new state-of-the-art in HPO by a statistically-significant margin. This paper introduces three main technical contributions:• We introduce a novel neural network BO surrogate (named Deep Ranking Ensembles) optimized with Learning-to-Rank (L2R) losses;• We propose a new technique for meta-learning our ensemble surrogate from large-scale public meta-datasets;• Deep Ranking Ensembles achieve the new state-of-the-art in HPO, demonstrated through a very large-scale experimental protocol. | Published as a conference paper at ICLR 2023 DEEP RANKING ENSEMBLES FOR HYPERPARAMETER OPTIMIZATION |
d199551982 | Neural text generation is a key tool in natural language applications, but it is well known there are major problems at its core. In particular, standard likelihood training and decoding leads to dull and repetitive responses(Holtzman et al., 2019). While some post-hoc fixes have been proposed, in particular top-k and nucleus sampling, they do not address the fact that the token-level probabilities predicted by the model itself are poor. In this paper we show that the likelihood objective itself is at fault, resulting in a model that assigns too much probability to sequences that contain repeats and frequent words unlike the human training distribution. We propose a new objective, unlikelihood training, which forces unlikely generations to be assigned lower probability by the model. We show that both token and sequence level unlikelihood training give less repetitive, less dull text while maintaining perplexity, giving far superior generations using standard greedy or beam search. Our approach provides a strong alternative to traditional training. * Equal contribution; the ordering was decided by a coin flip. | NEURAL TEXT DEGENERATION WITH UNLIKELIHOOD TRAINING |
d238856973 | Recent work (e.g. LAMA (Petroni et al., 2019)) has found that the quality of the factual information extracted from Large Language Models (LLMs) depends on the prompts used to query them. This inconsistency is problematic because different users will query LLMs for the same information using different wording, but should receive the same, accurate responses regardless. In this work we aim to address this shortcoming by introducing P-Adapters: lightweight models that sit between the embedding layer and first attention layer of LLMs. They take LLM embeddings as input and output continuous prompts that are used to query the LLM. Additionally, we investigate Mixture of Experts (MoE) models that learn a set of continuous prompts ("experts") and select one to query the LLM. They require a separate classifier trained on human-annotated data to map natural language prompts to the continuous ones. P-Adapters perform comparably to the more complex MoE models in extracting factual information from BERT and RoBERTa while eliminating the need for additional annotations. P-Adapters show between 12-26% absolute improvement in precision and 36-50% absolute improvement in consistency over a baseline of only using natural language queries. Finally, we investigate what makes P-Adapters successful and conclude that a significant factor is access to the LLM's embeddings of the original natural language prompt, particularly the subject of the entity pair being queried. * blnewman@cs.stanford.edu. Work conducted during an internship at Salesforce Research. | P-ADAPTERS: ROBUSTLY EXTRACTING FACTUAL IN- FORMATION FROM LANGUAGE MODELS WITH DI- VERSE PROMPTS |
d49310753 | We introduce a new algorithm for reinforcement learning called Maximum aposteriori Policy Optimisation (MPO) based on coordinate ascent on a relativeentropy objective. We show that several existing methods can directly be related to our derivation. We develop two off-policy algorithms and demonstrate that they are competitive with the state-of-the-art in deep reinforcement learning. In particular, for continuous control, our method outperforms existing methods with respect to sample efficiency, premature convergence and robustness to hyperparameter settings. , et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529-533, 2015. | Published as a conference paper at ICLR 2018 MAXIMUM A POSTERIORI POLICY OPTIMISATION |
d15207478 | 5Non-negative matrix factorization (NMF) has become a popular machine 6 learning approach to many problems in text mining, speech and image 7 processing, bio-informatics and seismic data analysis to name a few. In 8 NMF, a matrix of non-negative data is approximated by the low-rank 9product of two matrices with non-negative entries. In this paper, the 10 approximation quality is measured by the Kullback-Leibler divergence 11between the data and its low-rank reconstruction. The existence of the 12 simple multiplicative update (MU) algorithm for computing the matrix 13 factors has contributed to the success of NMF. Despite the availability of 14 algorithms showing faster convergence, MU remains popular due to its 15 simplicity. In this paper, a diagonalized Newton algorithm (DNA) is 16 proposed showing faster convergence while the implementation remains 17 simple and suitable for high-rank problems. The DNA algorithm is applied 18 to various publicly available data sets, showing a substantial speed-up on 19 modern hardware. 20 21 | The Diagonalized Newton Algorithm for Non- negative Matrix Factorization |
d256105559 | To alleviate the high annotation cost in LiDAR-based 3D object detection, active learning is a promising solution that learns to select only a small portion of unlabeled data to annotate, without compromising model performance. Our empirical study, however, suggests that mainstream uncertainty-based and diversitybased active learning policies are not effective when applied in the 3D detection task, as they fail to balance the trade-off between point cloud informativeness and box-level annotation costs. To overcome this limitation, we jointly investigate three novel criteria in our framework CRB for point cloud acquisitionlabel conciseness, feature representativeness and geometric balance, which hierarchically filters out the point clouds of redundant 3D bounding box labels, latent features and geometric characteristics (e.g., point cloud density) from the unlabeled sample pool and greedily selects informative ones with fewer objects to annotate. Our theoretical analysis demonstrates that the proposed criteria aligns the marginal distributions of the selected subset and the prior distributions of the unseen test set, and minimizes the upper bound of the generalization error. To validate the effectiveness and applicability of CRB, we conduct extensive experiments on the two benchmark 3D object detection datasets of KITTI and Waymo and examine both one-stage (i.e., SECOND) and two-stage 3D detectors (i.e., PV-RCNN). Experiments evidence that the proposed approach outperforms existing active learning strategies and achieves fully supervised performance requiring 1% and 8% annotations of bounding boxes and point clouds, respectively. Source code: https://github.com/Luoyadan/CRB-active-3Ddet. | Published as a conference paper at ICLR 2023 EXPLORING ACTIVE 3D OBJECT DETECTION FROM A GENERALIZATION PERSPECTIVE |
d3062491 | Convolutional autoregressive models have recently demonstrated state-of-the-art performance on a number of generation tasks. While fast, parallel training methods have been crucial for their success, generation is typically implemented in a naïve fashion where redundant computations are unnecessarily repeated. This results in slow generation, making such models infeasible for production environments. In this work, we describe a method to speed up generation in convolutional autoregressive models. The key idea is to cache hidden states to avoid redundant computation. We apply our fast generation method to the Wavenet and PixelCNN++ models and achieve up to 21× and 183× speedups respectively. * Denotes equal contribution. 1 https://github.com/tomlepaine/fast-wavenet 2 https://github.com/PrajitR/fast-pixel-cnn | Workshop track -ICLR 2017 FAST GENERATION FOR CONVOLUTIONAL AUTOREGRESSIVE MODELS |
d256808230 | Neural image compression has surpassed state-of-the-art traditional codecs (H.266/VVC) for rate-distortion (RD) performance, but suffers from large complexity and separate models for different rate-distortion trade-offs. In this paper, we propose an Efficient single-model Variable-bit-rate Codec (EVC), which is able to run at 30 FPS with 768x512 input images and still outperforms VVC for the RD performance. By further reducing both encoder and decoder complexities, our small model even achieves 30 FPS with 1920x1080 input images. To bridge the performance gap between our different capacities models, we meticulously design the mask decay, which transforms the large model's parameters into the small model automatically. And a novel sparsity regularization loss is proposed to mitigate shortcomings of L p regularization. Our algorithm significantly narrows the performance gap by 50% and 30% for our medium and small models, respectively. At last, we advocate the scalable encoder for neural image compression. The encoding complexity is dynamic to meet different latency requirements. We propose decaying the large encoder multiple times to reduce the residual representation progressively. Both mask decay and residual representation learning greatly improve the RD performance of our scalable encoder. Our code is at https://github.com/microsoft/DCVC. | EVC: TOWARDS REAL-TIME NEURAL IMAGE COM- PRESSION WITH MASK DECAY |
d224803224 | In this paper, we introduce Watch-And-Help (WAH), a challenge for testing social intelligence in agents. In WAH, an AI agent needs to help a human-like agent perform a complex household task efficiently. To succeed, the AI agent needs to i) understand the underlying goal of the task by watching a single demonstration of the human-like agent performing the same task (social perception), and ii) coordinate with the human-like agent to solve the task in an unseen environment as fast as possible (human-AI collaboration). For this challenge, we build VirtualHome-Social, a multi-agent household environment, and provide a benchmark including both planning and learning based baselines. We evaluate the performance of AI agents with the human-like agent as well as with real humans using objective metrics and subjective user ratings. Experimental results demonstrate that the proposed challenge and virtual environment enable a systematic evaluation on the important aspects of machine social intelligence at scale. 1 | WATCH-AND-HELP: A CHALLENGE FOR SOCIAL PER- CEPTION AND HUMAN-AI COLLABORATION |
d256416448 | Monocular scene reconstruction from posed images is challenging due to the complexity of a large environment. Recent volumetric methods learn to directly predict the TSDF volume and have demonstrated promising results in this task. However, most methods focus on how to extract and fuse the 2D features to a 3D feature volume, but none of them improve the way how the 3D volume is aggregated. In this work, we propose an SDF transformer network, which replaces the role of 3D CNN for better 3D feature aggregation. To reduce the explosive computation complexity of the 3D multi-head attention, we propose a sparse window attention module, where the attention is only calculated between the non-empty voxels within a local window. Then a top-down-bottom-up 3D attention network is built for 3D feature aggregation, where a dilate-attention structure is proposed to prevent geometry degeneration, and two global modules are employed to equip with global receptive fields. The experiments on multiple datasets show that this 3D transformer network generates a more accurate and complete reconstruction, which outperforms previous methods by a large margin. Remarkably, the mesh accuracy is improved by 41.8%, and the mesh completeness is improved by 25.3% on the ScanNet dataset. | Published as a conference paper at ICLR 2023 3D FORMER: MONOCULAR SCENE RECONSTRUCTION WITH 3D SDF TRANSFORMERS |
d248377370 | Reasoning about visual relationships is central to how humans interpret the visual world. This task remains challenging for current deep learning algorithms since it requires addressing three key technical problems jointly: 1) identifying object entities and their properties, 2) inferring semantic relations between pairs of entities, and 3) generalizing to novel object-relation combinations, i.e. systematic generalization. In this work, we use vision transformers (ViTs) as our base model for visual reasoning and make better use of concepts defined as object entities and their relations to improve the reasoning ability of ViTs. Specifically, we introduce a novel concept-feature dictionary to allow flexible image feature retrieval at training time with concept keys. This dictionary enables two new conceptguided auxiliary tasks: 1) a global task for promoting relational reasoning, and 2) a local task for facilitating semantic object-centric correspondence learning. To examine the systematic generalization of visual reasoning models, we introduce systematic splits for the standard HICO and GQA benchmarks. We show the resulting model, Concept-guided Vision Transformer (or RelViT for short) significantly outperforms prior approaches on HICO and GQA by 16% and 13% in the original split, and by 43% and 18% in the systematic split. Our ablation analyses also reveal our model's compatibility with multiple ViT variants and robustness to hyper-parameters. Code is available. | Published as a conference paper at ICLR 2022 RELVIT: CONCEPT-GUIDED VISION TRANSFORMER FOR VISUAL RELATIONAL REASONING |
d211677318 | Unsupervised text encoding models have recently fueled substantial progress in Natural Language Processing (NLP). The key idea is to use neural networks to convert words in texts to vector space representations (embeddings) based on word positions in a sentence and their contexts, which are suitable for end-to-end training of downstream tasks. We see a strikingly similar situation in spatial analysis, which focuses on incorporating both absolute positions and spatial contexts of geographic objects such as Points of Interest (POIs) into models. A general-purpose representation model for space is valuable for a multitude of tasks. However, no such general model exists to date beyond simply applying discretization or feedforward nets to coordinates, and little effort has been put into jointly modeling distributions with vastly different characteristics, which commonly emerges from GIS data. Meanwhile, Nobel Prize-winning Neuroscience research shows that grid cells in mammals provide a multi-scale periodic representation that functions as a metric for location encoding and is critical for recognizing places and for path-integration. Therefore, we propose a representation learning model called Space2Vec to encode the absolute positions and spatial relationships of places. We conduct experiments on two real-world geographic data for two different tasks: 1) predicting types of POIs given their positions and context, 2) image classification leveraging their geo-locations. Results show that because of its multiscale representations, Space2Vec outperforms well-established ML approaches such as RBF kernels, multi-layer feed-forward nets, and tile embedding approaches for location modeling and image classification tasks. Detailed analysis shows that all baselines can at most well handle distribution at one scale but show poor performances in other scales. In contrast, Space2Vec 's multi-scale representation can handle distributions at different scales. 1 1 Link to project repository: https://github.com/gengchenmai/space2vec | MULTI-SCALE REPRESENTATION LEARNING FOR SPA- TIAL FEATURE DISTRIBUTIONS USING GRID CELLS |
d246822749 | Advanced representation learning techniques require reliable and general evaluation methods. Recently, several algorithms based on the common idea of geometric and topological analysis of a manifold approximated from the learned data representations have been proposed. In this work, we introduce Delaunay Component Analysis (DCA) -an evaluation algorithm which approximates the data manifold using a more suitable neighbourhood graph called Delaunay graph. This provides a reliable manifold estimation even for challenging geometric arrangements of representations such as clusters with varying shape and density as well as outliers, which is where existing methods often fail. Furthermore, we exploit the nature of Delaunay graphs and introduce a framework for assessing the quality of individual novel data representations. We experimentally validate the proposed DCA method on representations obtained from neural networks trained with contrastive objective, supervised and generative models, and demonstrate various use cases of our extended single point evaluation framework. | Published as a conference paper at ICLR 2022 DELAUNAY COMPONENT ANALYSIS FOR EVALUATION OF DATA REPRESENTATIONS |
d256390161 | We present a subset selection algorithm designed to work with arbitrary model families in a practical batch setting. In such a setting, an algorithm can sample examples one at a time but, in order to limit overhead costs, is only able to update its state (i.e. further train model weights) once a large enough batch of examples is selected. Our algorithm, IWeS, selects examples by importance sampling where the sampling probability assigned to each example is based on the entropy of models trained on previously selected batches. IWeS admits significant performance improvement compared to other subset selection algorithms for seven publicly available datasets. Additionally, it is competitive in an active learning setting, where the label information is not available at selection time. We also provide an initial theoretical analysis to support our importance weighting approach, proving generalization and sampling rate bounds. | Published as a conference paper at ICLR 2023 LEVERAGING IMPORTANCE WEIGHTS IN SUBSET SE- LECTION |
d11322298 | Hyper-parameter selection remains a daunting task when building a pattern recognition architecture which performs well, particularly in recently constructed visual pipeline models for feature extraction. We re-formulate pooling in an existing pipeline as a function of adjustable pooling map weight parameters and propose the use of supervised error signals from gradient descent to tune the established maps within the model. This technique allows us to learn what would otherwise be a design choice within the model and specialize the maps to aggregate areas of invariance for the task presented. Preliminary results show moderate potential gains in classification accuracy and highlight areas of importance within the intermediate feature representation space. | Gradient Driven Learning for Pooling in Visual Pipeline Feature Extraction Models |
d222291168 | Massively multilingual models subsuming tens or even hundreds of languages pose great challenges to multi-task optimization. While it is a common practice to apply a language-agnostic procedure optimizing a joint multilingual task objective, how to properly characterize and take advantage of its underlying problem structure for improving optimization efficiency remains under-explored. In this paper, we attempt to peek into the black-box of multilingual optimization through the lens of loss function geometry. We find that gradient similarity measured along the optimization trajectory is an important signal, which correlates well with not only language proximity but also the overall model performance. Such observation helps us to identify a critical limitation of existing gradient-based multi-task learning methods, and thus we derive a simple and scalable optimization procedure, named Gradient Vaccine, which encourages more geometrically aligned parameter updates for close tasks. Empirically, our method obtains significant model performance gains on multilingual machine translation and XTREME benchmark tasks for multilingual language models. Our work reveals the importance of properly measuring and utilizing language proximity in multilingual optimization, and has broader implications for multi-task learning beyond multilingual modeling. * Work done during an internship at Google. | GRADIENT VACCINE: INVESTIGATING AND IMPROV- ING MULTI-TASK OPTIMIZATION IN MASSIVELY MUL- TILINGUAL MODELS |
d222125075 | Energy-based models (EBMs) have recently been successful in representing complex distributions of small images. However, sampling from them requires expensive Markov chain Monte Carlo (MCMC) iterations that mix slowly in high dimensional pixel space. Unlike EBMs, variational autoencoders (VAEs) generate samples quickly and are equipped with a latent space that enables fast traversal of the data manifold. However, VAEs tend to assign high probability density to regions in data space outside the actual data distribution and often fail at generating sharp images. In this paper, we propose VAEBM, a symbiotic composition of a VAE and an EBM that offers the best of both worlds. VAEBM captures the overall mode structure of the data distribution using a state-of-the-art VAE and it relies on its EBM component to explicitly exclude non-data-like regions from the model and refine the image samples. Moreover, the VAE component in VAEBM allows us to speed up MCMC updates by reparameterizing them in the VAE's latent space. Our experimental results show that VAEBM outperforms state-of-the-art VAEs and EBMs in generative quality on several benchmark image datasets by a large margin. It can generate high-quality images as large as 256×256 pixels with short MCMC chains. We also demonstrate that VAEBM provides complete mode coverage and performs well in out-of-distribution detection. The source code is available at https://github.com/NVlabs/VAEBM * Work done during an internship at NVIDIA arXiv:2010.00654v3 [cs.LG] | VAEBM: A SYMBIOSIS BETWEEN VARIATIONAL AU- TOENCODERS AND ENERGY-BASED MODELS |
d257254877 | Despite that going deep has proven successful in many neural architectures, the existing graph transformers are relatively shallow. In this work, we explore whether more layers are beneficial to graph transformers, and find that current graph transformers suffer from the bottleneck of improving performance by increasing depth. Our further analysis reveals the reason is that deep graph transformers are limited by the vanishing capacity of global attention, restricting the graph transformer from focusing on the critical substructure and obtaining expressive features. To this end, we propose a novel graph transformer model named DeepGraph that explicitly employs substructure tokens in the encoded representation, and applies local attention on related nodes to obtain substructure based attention encoding. Our model enhances the ability of the global attention to focus on substructures and promotes the expressiveness of the representations, addressing the limitation of self-attention as the graph transformer deepens. Experiments show that our method unblocks the depth limitation of graph transformers and results in stateof-the-art performance across various graph benchmarks with deeper models. | Published as a conference paper at ICLR 2023 ARE MORE LAYERS BENEFICIAL TO GRAPH TRANS- FORMERS? |
d229371253 | We describe the convex semi-infinite dual of the two-layer vector-output ReLU neural network training problem. This semi-infinite dual admits a finite dimensional representation, but its support is over a convex set which is difficult to characterize. In particular, we demonstrate that the non-convex neural network training problem is equivalent to a finite-dimensional convex copositive program. Our work is the first to identify this strong connection between the global optima of neural networks and those of copositive programs. We thus demonstrate how neural networks implicitly attempt to solve copositive programs via semi-nonnegative matrix factorization, and draw key insights from this formulation. We describe the first algorithms for provably finding the global minimum of the vector output neural network training problem, which are polynomial in the number of samples for a fixed data rank, yet exponential in the dimension. However, in the case of convolutional architectures, the computational complexity is exponential in only the filter size and polynomial in all other parameters. We describe the circumstances in which we can find the global optimum of this neural network training problem exactly with soft-thresholded SVD, and provide a copositive relaxation which is guaranteed to be exact for certain classes of problems, and which corresponds with the solution of Stochastic Gradient Descent in practice. | VECTOR-OUTPUT RELU NEURAL NETWORK PROB- LEMS ARE COPOSITIVE PROGRAMS: CONVEX ANAL- YSIS OF TWO LAYER NETWORKS AND POLYNOMIAL- TIME ALGORITHMS |
d1923568 | Policies for complex visual tasks have been successfully learned with deep reinforcement learning, using an approach called deep Q-networks (DQN), but relatively large (task-specific) networks and extensive training are needed to achieve good performance. In this work, we present a novel method called policy distillation that can be used to extract the policy of a reinforcement learning agent and train a new network that performs at the expert level while being dramatically smaller and more efficient. Furthermore, the same method can be used to consolidate multiple task-specific policies into a single policy. We demonstrate these claims using the Atari domain and show that the multi-task distilled agent outperforms the single-task teachers as well as a jointly-trained DQN agent. | POLICY DISTILLATION |
d254974335 | We propose a new neural network design paradigm Reversible Column Network (RevCol). The main body of RevCol is composed of multiple copies of subnetworks, named columns respectively, between which multi-level reversible connections are employed. Such architectural scheme attributes RevCol very different behavior from conventional networks: during forward propagation, features in RevCol are learned to be gradually disentangled when passing through each column, whose total information is maintained rather than compressed or discarded as other network does. Our experiments suggest that CNN-style RevCol models can achieve very competitive performances on multiple computer vision tasks such as image classification, object detection and semantic segmentation, especially with large parameter budget and large dataset. For example, after ImageNet-22K pre-training, RevCol-XL obtains 88.2% ImageNet-1K accuracy. Given more pre-training data, our largest model RevCol-H reaches 90.0% on ImageNet-1K, 63.8% AP box on COCO detection minival set, 61.0% mIoU on ADE20k segmentation. To our knowledge, it is the best COCO detection and ADE20k segmentation result among pure (static) CNN models. Moreover, as a general macro architecture fashion, RevCol can also be introduced into transformers or other neural networks, which is demonstrated to improve the performances in both computer vision and NLP tasks. We release code and models at https://github.com/megvii-research/RevCol * | Published as a conference paper at ICLR 2023 REVERSIBLE COLUMN NETWORKS MEGVII Technology 1 Beijing Academy of Artificial Intelligence |
d244908535 | This paper proposes an algorithm, RMDA, for training neural networks (NNs) with a regularization term for promoting desired structures. RMDA does not incur computation additional to proximal SGD with momentum, and achieves variance reduction without requiring the objective function to be of the finite-sum form. Through the tool of manifold identification from nonlinear optimization, we prove that after a finite number of iterations, all iterates of RMDA possess a desired structure identical to that induced by the regularizer at the stationary point of asymptotic convergence, even in the presence of engineering tricks like data augmentation that complicate the training process. Experiments on training NNs with structured sparsity confirm that variance reduction is necessary for such an identification, and show that RMDA thus significantly outperforms existing methods for this task. For unstructured sparsity, RMDA also outperforms a state-of-the-art pruning method, validating the benefits of training structured NNs through regularization. Implementation of RMDA is available at https://www.github.com/zihsyuan1214/rmda. Patrick Breheny and Jian Huang. Penalized methods for bi-level variable selection. Statistics and its interface, 2(3): 369, 2009. 19 | Training Structured Neural Networks Through Mani- fold Identification and Variance Reduction |
d232168371 | Deep object recognition models have been very successful over benchmark datasets such as ImageNet. How accurate and robust are they to distribution shifts arising from natural and synthetic variations in datasets? Prior research on this problem has primarily focused on ImageNet variations (e.g., ImageNetV2, ImageNet-A). To avoid potential inherited biases in these studies, we take a different approach. Specifically, we reanalyze the ObjectNet dataset 1 recently proposed by Barbu et al. containing objects in daily life situations. They showed a dramatic performance drop of the state of the art object recognition models on this dataset. Due to the importance and implications of their results regarding the generalization ability of deep models, we take a second look at their analysis. We find that applying deep models to the isolated objects, rather than the entire scene as is done in the original paper, results in around 20-30% performance improvement. Relative to the numbers reported in Barbu et al., around 10-15% of the performance loss is recovered, without any test time data augmentation. Despite this gain, however, we conclude that deep models still suffer drastically on the ObjectNet dataset. We also investigate the robustness of models against synthetic image perturbations such as geometric transformations (e.g., scale, rotation, translation), natural image distortions (e.g., impulse noise, blur) as well as adversarial attacks (e.g., FGSM and PGD-5). Our results indicate that limiting the object area as much as possible (i.e., from the entire image to the bounding box to the segmentation mask) leads to consistent improvement in accuracy and robustness. Finally, through a qualitative analysis of ObjectNet data, we find that i) a large number of images in this dataset are hard to recognize even for humans, and ii) easy (hard) samples for models match with easy (hard) samples for humans. Overall, our analyses show that ObjecNet is still a challenging test platform for evaluating the generalization ability of models. Code and data are available at https://github.com/aliborji/ObjectNetReanalysis.git 2 .1 https://objectnet.dev/ 2 See https://openreview.net/forum?id=Q4EUywJIkqr for reviews and discussions. A prelimnary version of this work has been published in Arxiv (Borji, 2020).3 Classification of an object appearing lonely in an image. For images containing multiple objects, object localization or detection is required first. . ilab-20m: A large-scale controlled object dataset to investigate deep learning. In . Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. arXiv preprint arXiv:1811.12231, 2018a. | Published as a conference paper at ICLR 2021 CONTEMPLATING REAL-WORLD OBJECT CLASSIFICATION |
d246240825 | Graph-based next-step prediction models have recently been very successful in modeling complex high-dimensional physical systems on irregular meshes. However, due to their short temporal attention span, these models suffer from error accumulation and drift. In this paper, we propose a new method that captures long-term dependencies through a transformer-style temporal attention model. We introduce an encoder-decoder structure to summarize features and create a compact mesh representation of the system state, to allow the temporal model to operate on a low-dimensional mesh representations in a memory efficient manner. Our method outperforms a competitive GNN baseline on several complex fluid dynamics prediction tasks, from sonic shocks to vascular flow. We demonstrate stable rollouts without the need for training noise and show perfectly phase-stable predictions even for very long sequences. More broadly, we believe our approach paves the way to bringing the benefits of attention-based sequence models to solving high-dimensional complex physics tasks. * Equal contribution. | Published as a conference paper at ICLR 2022 PREDICTING PHYSICS IN MESH-REDUCED SPACE WITH TEMPORAL ATTENTION |
d14228101 | This article exposes the failure of some big neural networks to leverage added capacity to reduce underfitting. Past research suggest diminishing returns when increasing the size of neural networks. Our experiments on ImageNet LSVRC-2010 show that this may be due to the fact there are highly diminishing returns for capacity in terms of training error, leading to underfitting. This suggests that the optimization method -first order gradient descent -fails at this regime. Directly attacking this problem, either through the optimization method or the choices of parametrization, may allow to improve the generalization error on large datasets, for which a large capacity is required. | Big Neural Networks Waste Capacity |
d238744253 | Molecular design and synthesis planning are two critical steps in the process of molecular discovery that we propose to formulate as a single shared task of conditional synthetic pathway generation. We report an amortized approach to generate synthetic pathways as a Markov decision process conditioned on a target molecular embedding. This approach allows us to conduct synthesis planning in a bottom-up manner and design synthesizable molecules by decoding from optimized conditional codes, demonstrating the potential to solve both problems of design and synthesis simultaneously. The approach leverages neural networks to probabilistically model the synthetic trees, one reaction step at a time, according to reactivity rules encoded in a discrete action space of reaction templates. We train these networks on hundreds of thousands of artificial pathways generated from a pool of purchasable compounds and a list of expert-curated templates. We validate our method with (a) the recovery of molecules using conditional generation, (b) the identification of synthesizable structural analogs, and (c) the optimization of molecular structures given oracle functions relevant to drug discovery. | Published as a conference paper at ICLR 2022 AMORTIZED TREE GENERATION FOR BOTTOM-UP SYNTHESIS PLANNING AND SYNTHESIZABLE MOLECULAR DESIGN |
d246823485 | Understanding the performance of machine learning models across diverse data distributions is critically important for reliable applications. Motivated by this, there is a growing focus on curating benchmark datasets that capture distribution shifts. While valuable, the existing benchmarks are limited in that many of them only contain a small number of shifts and they lack systematic annotation about what is different across different shifts. We present MetaShift-a collection of 12,868 sets of natural images across 410 classes-to address this challenge. We leverage the natural heterogeneity of Visual Genome and its annotations to construct MetaShift.The key construction idea is to cluster images using its metadata, which provides context for each image (e.g. cats with cars or cats in bathroom) that represent distinct data distributions. MetaShift has two important benefits: first, it contains orders of magnitude more natural data shifts than previously available. Second, it provides explicit explanations of what is unique about each of its data sets and a distance score that measures the amount of distribution shift between any two of its data sets. We demonstrate the utility of MetaShift in benchmarking several recent proposals for training models to be robust to data shifts. We find that the simple empirical risk minimization performs the best when shifts are moderate and no method had a systematic advantage for large shifts. We also show how MetaShift can help to visualize conflicts between data subsets during model training 1 . | METASHIFT: A DATASET OF DATASETS FOR EVALUAT- ING CONTEXTUAL DISTRIBUTION SHIFTS AND TRAIN- ING CONFLICTS |
d254069728 | Graph Neural Networks usually rely on the assumption that the graph topology is available to the network as well as optimal for the downstream task. Latent graph inference allows models to dynamically learn the intrinsic graph structure of problems where the connectivity patterns of data may not be directly accessible. In this work, we generalize the discrete Differentiable Graph Module (dDGM) for latent graph learning. The original dDGM architecture used the Euclidean plane to encode latent features based on which the latent graphs were generated. By incorporating Riemannian geometry into the model and generating more complex embedding spaces, we can improve the performance of the latent graph inference system. In particular, we propose a computationally tractable approach to produce product manifolds of constant curvature model spaces that can encode latent features of varying structure. The latent representations mapped onto the inferred product manifold are used to compute richer similarity measures that are leveraged by the latent graph learning model to obtain optimized latent graphs. Moreover, the curvature of the product manifold is learned during training alongside the rest of the network parameters and based on the downstream task, rather than it being a static embedding space. Our novel approach is tested on a wide range of datasets, and outperforms the original dDGM model. | Published as a conference paper at ICLR 2023 LATENT GRAPH INFERENCE USING PRODUCT MANIFOLDS |
d233444001 | In this paper we consider reinforcement learning tasks with progressive rewards; that is, tasks where the rewards tend to increase in magnitude over time. We hypothesise that this property may be problematic for value-based deep reinforcement learning agents, particularly if the agent must first succeed in relatively unrewarding regions of the task in order to reach more rewarding regions. To address this issue, we propose Spectral DQN, which decomposes the reward into frequencies such that the high frequencies only activate when large rewards are found. This allows the training loss to be balanced so that it gives more even weighting across small and large reward regions. In two domains with extreme reward progressivity, where standard value-based methods struggle significantly, Spectral DQN is able to make much farther progress. Moreover, when evaluated on a set of six standard Atari games that do not overtly favour the approach, Spectral DQN remains more than competitive: While it underperforms one of the benchmarks in a single game, it comfortably surpasses the benchmarks in three games. These results demonstrate that the approach is not overfit to its target problem, and suggest that Spectral DQN may have advantages beyond addressing reward progressivity. | Published as a conference paper at ICLR 2021 ADAPTING TO REWARD PROGRESSIVITY VIA SPECTRAL REINFORCEMENT LEARNING |
d14915449 | We introduce a new test of how well language models capture meaning in children's books. Unlike standard language modelling benchmarks, it distinguishes the task of predicting syntactic function words from that of predicting lowerfrequency words, which carry greater semantic content. We compare a range of state-of-the-art models, each with a different way of encoding what has been previously read. We show that models which store explicit representations of long-term contexts outperform state-of-the-art neural language models at predicting semantic content words, although this advantage is not observed for syntactic function words. Interestingly, we find that the amount of text encoded in a single memory representation is highly influential to the performance: there is a sweet-spot, not too big and not too small, between single words and full sentences that allows the most meaningful information in a text to be effectively retained and recalled. Further, the attention over such window-based memories can be trained effectively through self-supervision. We then assess the generality of this principle by applying it to the CNN QA benchmark, which involves identifying named entities in paraphrased summaries of news articles, and achieve state-of-the-art performance. | THE GOLDILOCKS PRINCIPLE: READING CHILDREN'S BOOKS WITH EXPLICIT MEMORY REPRESENTATIONS |
d204206524 | Reinforcement learning requires manual specification of a reward function to learn a task. While in principle this reward function only needs to specify the task goal, in practice reinforcement learning can be very time-consuming or even infeasible unless the reward function is shaped so as to provide a smooth gradient towards a successful outcome. This shaping is difficult to specify by hand, particularly when the task is learned from raw observations, such as images. In this paper, we study how we can automatically learn dynamical distances: a measure of the expected number of time steps to reach a given goal state from any other state. These dynamical distances can be used to provide well-shaped reward functions for reaching new goals, making it possible to learn complex tasks efficiently. We show that dynamical distances can be used in a semi-supervised regime, where unsupervised interaction with the environment is used to learn the dynamical distances, while a small amount of preference supervision is used to determine the task goal, without any manually engineered reward function or goal examples. We evaluate our method both on a real-world robot and in simulation. We show that our method can learn to turn a valve with a real-world 9-DoF hand, using raw image observations and just ten preference labels, without any other supervision. Videos of the learned skills can be found on the project website: https://sites.google.com/view/dynamical-distance-learning. | Published as a conference paper at ICLR 2020 DYNAMICAL DISTANCE LEARNING FOR SEMI-SUPERVISED AND UNSUPERVISED SKILL DISCOVERY |
d253446984 | Many datasets are biased, namely they contain easy-to-learn features that are highly correlated with the target class only in the dataset but not in the true underlying distribution of the data. For this reason, learning unbiased models from biased data has become a very relevant research topic in the last years. In this work, we tackle the problem of learning representations that are robust to biases. We first present a margin-based theoretical framework that allows us to clarify why recent contrastive losses (InfoNCE, SupCon, etc.) can fail when dealing with biased data. Based on that, we derive a novel formulation of the supervised contrastive loss ( -SupInfoNCE), providing more accurate control of the minimal distance between positive and negative samples. Furthermore, thanks to our theoretical framework, we also propose FairKL, a new debiasing regularization loss, that works well even with extremely biased data. We validate the proposed losses on standard vision datasets including CIFAR10, CIFAR100, and ImageNet, and we assess the debiasing capability of FairKL with -SupInfoNCE, reaching stateof-the-art performance on a number of biased datasets, including real instances of biases "in the wild". * Corresponding author: carlo.barbano@unito.it 1 We refer to any contrastive loss and not necessarily to losses based on pairs of samples as in(Sohn, 2016). | Published as a conference paper at ICLR 2023 UNBIASED SUPERVISED CONTRASTIVE LEARNING |
d256390383 | We introduce Progressive Prompts -a simple and efficient approach for continual learning in language models. Our method allows forward transfer and resists catastrophic forgetting, without relying on data replay or a large number of task-specific parameters. Progressive Prompts learns a new soft prompt for each task and sequentially concatenates it with the previously learned prompts, while keeping the base model frozen. Experiments on standard continual learning benchmarks show that our approach outperforms state-of-the-art methods, with an improvement >20% in average test accuracy over the previous best-preforming method on T5 model. We also explore a more challenging continual learning setup with longer sequences of tasks and show that Progressive Prompts significantly outperforms prior methods. * Work done during Meta AI research internship. | Published as a conference paper at ICLR 2023 PROGRESSIVE PROMPTS: CONTINUAL LEARNING FOR LANGUAGE MODELS |
d259224350 | Most offline reinforcement learning (RL) algorithms return a target policy maximizing a trade-off between (1) the expected performance gain over the behavior policy that collected the dataset, and (2) the risk stemming from the out-of-distributionness of the induced state-action occupancy. It follows that the performance of the target policy is strongly related to the performance of the behavior policy and, thus, the trajectory return distribution of the dataset. We show that in mixed datasets consisting of mostly low-return trajectories and minor high-return trajectories, stateof-the-art offline RL algorithms are overly restrained by low-return trajectories and fail to exploit high-performing trajectories to the fullest. To overcome this issue, we show that, in deterministic MDPs with stochastic initial states, the dataset sampling can be re-weighted to induce an artificial dataset whose behavior policy has a higher return. This re-weighted sampling strategy may be combined with any offline RL algorithm. We further analyze that the opportunity for performance improvement over the behavior policy correlates with the positive-sided variance of the returns of the trajectories in the dataset. We empirically show that while CQL, IQL, and TD3+BC achieve only a part of this potential policy improvement, these same algorithms combined with our reweighted sampling strategy fully exploit the dataset. Furthermore, we empirically demonstrate that, despite its theoretical limitation, the approach may still be efficient in stochastic environments. The code is available at https://github.com/Improbable-AI/harness-offline-rl. | Published as a conference paper at ICLR 2023 HARNESSING MIXED OFFLINE REINFORCEMENT LEARNING DATASETS VIA TRAJECTORY WEIGHTING |
d46928091 | In this paper, we introduce a system called GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant. Interactive theorem provers such as Coq enable users to construct machine-checkable proofs in a step-by-step manner. Hence, they provide an opportunity to explore theorem proving with human supervision. We use GamePad to synthesize proofs for a simple algebraic rewrite problem and train baseline models for a formalization of the Feit-Thompson theorem. We address position evaluation (i.e., predict the number of proof steps left) and tactic prediction (i.e., predict the next proof step) tasks, which arise naturally in tactic-based theorem proving. | GAMEPAD: A LEARNING ENVIRONMENT FOR THEO- REM PROVING |
d201486 | paper at ICLR 2015 EXAMPLE SELECTION FOR DICTIONARY LEARNING
29 Dec 20140B64DBCA30B4E43D251A3DD02A3D5414arXiv:1412.6177v2[cs.LG]
In unsupervised learning, an unbiased uniform sampling strategy is typically used, in order that the learned features faithfully encode the statistical structure of the training data.In this work, we explore whether active example selection strategies -algorithms that select which examples to use, based on the current estimate of the features -can accelerate learning.Specifically, we investigate effects of heuristic and saliency-inspired selection algorithms on the dictionary learning task with spars | Under review as a conference paper at ICL |
d1163588 | We introduce a hybrid CPU/GPU version of the Asynchronous Advantage Actor-Critic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming tasks. We analyze its computational traits and concentrate on aspects critical to leveraging the GPU's computational power. We introduce a system of queues and a dynamic scheduling strategy, potentially helpful for other asynchronous algorithms as well. Our hybrid CPU/GPU version of A3C, based on TensorFlow, achieves a significant speed up compared to a CPU implementation; we make it publicly available to other researchers at | Published as a conference paper at ICLR 2017 REINFORCEMENT LEARNING THROUGH ASYN- CHRONOUS ADVANTAGE ACTOR-CRITIC ON A GPU |
d211020900 | How to generate descriptions from structured data organized in tables? Existing approaches using neural encoder-decoder models often suffer from lacking diversity. We claim that an open set of templates is crucial for enriching the phrase constructions and realizing varied generations. Learning such templates is prohibitive since it often requires a large paired <table,description> corpus, which is seldom available. This paper explores the problem of automatically learning reusable "templates" from paired and non-paired data. We propose the variational template machine (VTM), a novel method to generate text descriptions from data tables. Our contributions include: a) we carefully devise a specific model architecture and losses to explicitly disentangle text template and semantic content information in the latent spaces, and b) we utilize both small parallel data and large raw text without aligned tables to enrich the template learning. Experiments on datasets from a variety of different domains show that VTM is able to generate more diversely while keeping a good fluency and quality. | Published as a conference paper at ICLR 2020 VARIATIONAL TEMPLATE MACHINE FOR DATA-TO- TEXT GENERATION |
d257366019 | We develop an interior-point approach to solve constrained variational inequality (cVI) problems. Inspired by the efficacy of the alternating direction method of multipliers (ADMM) method in the single-objective context, we generalize ADMM to derive a first-order method for cVIs, that we refer to as ADMM-based interiorpoint method for constrained VIs (ACVI). We provide convergence guarantees for ACVI in two general classes of problems: (i) when the operator is ξ-monotone, and (ii) when it is monotone, some constraints are active and the game is not purely rotational. When the operator is, in addition, L-Lipschitz for the latter case, we match known lower bounds on rates for the gap function of O(1/ √ K) and O(1/K) for the last and average iterate, respectively. To the best of our knowledge, this is the first presentation of a first-order interior-point method for the general cVI problem that has a global convergence guarantee. Moreover, unlike previous work in this setting, ACVI provides a means to solve cVIs when the constraints are nontrivial. Empirical analyses demonstrate clear advantages of ACVI over common first-order methods. In particular, (i) cyclical behavior is notably reduced as our methods approach the solution from the analytic center, and (ii) unlike projection-based methods that zigzag when near a constraint, ACVI efficiently handles the constraints. * All authors contributed equally. Link to source code: https://github.com/Chavdarova/ACVI. | Published as a conference paper at ICLR 2023 SOLVING CONSTRAINED VARIATIONAL INEQUALITIES VIA A FIRST-ORDER INTERIOR POINT-BASED METHOD |
d256615229 | Few-shot class-incremental learning (FSCIL) has been a challenging problem as only a few training samples are accessible for each novel class in the new sessions. Finetuning the backbone or adjusting the classifier prototypes trained in the prior sessions would inevitably cause a misalignment between the feature and classifier of old classes, which explains the well-known catastrophic forgetting problem. In this paper, we deal with this misalignment dilemma in FSCIL inspired by the recently discovered phenomenon named neural collapse, which reveals that the last-layer features of the same class will collapse into a vertex, and the vertices of all classes are aligned with the classifier prototypes, which are formed as a simplex equiangular tight frame (ETF). It corresponds to an optimal geometric structure for classification due to the maximized Fisher Discriminant Ratio. We propose a neural collapse inspired framework for FSCIL. A group of classifier prototypes are pre-assigned as a simplex ETF for the whole label space, including the base session and all the incremental sessions. During training, the classifier prototypes are not learnable, and we adopt a novel loss function that drives the features into their corresponding prototypes. Theoretical analysis shows that our method holds the neural collapse optimality and does not break the feature-classifier alignment in an incremental fashion. Experiments on the miniImageNet, CUB-200, and CIFAR-100 datasets demonstrate that our proposed framework outperforms the state-of-the-art performances. Code address: https://github.com/NeuralCollapseApplications/FSCIL | Published as a conference paper at ICLR 2023 NEURAL COLLAPSE INSPIRED FEATURE-CLASSIFIER ALIGNMENT FOR FEW-SHOT CLASS INCREMENTAL LEARNING |
d202888885 | Frankle & Carbin, 2019)shows that there exist winning tickets (small but critical subnetworks) for dense, randomly initialized networks, that can be trained alone to achieve a comparable accuracy to the latter in a similar number of iterations. However, the identification of these winning tickets still requires the costly train-prune-retrain process, limiting their practical benefits. In this paper, we discover for the first time that the winning tickets can be identified at a very early training stage, which we term as Early-Bird (EB) tickets, via lowcost training schemes (e.g., early stopping and low-precision training) at large learning rates. Our finding on the existence of EB tickets is consistent with recently reported observations that the key connectivity patterns of neural networks emerge early. Furthermore, we propose a mask distance metric that can be used to identify EB tickets with a low computational overhead, without needing to know the true winning tickets that emerge after the full training. Finally, we leverage the existence of EB tickets and the proposed mask distance to develop efficient training methods, which are achieved by first identifying EB tickets via low-cost schemes, and then continuing to train merely the EB tickets towards the target accuracy. Experiments based on various deep networks and datasets validate: 1) the existence of EB tickets and the effectiveness of mask distance in efficiently identifying them; and 2) that the proposed efficient training via EB tickets can achieve up to 5.8× ∼ 10.7× energy savings while maintaining comparable or even better accuracy as compared to the most competitive state-ofthe-art training methods, demonstrating a promising and easily adopted method for tackling the often cost-prohibitive deep network training. | Published as a conference paper at ICLR 2020 DRAWING EARLY-BIRD TICKETS: TOWARDS MORE EF- FICIENT TRAINING OF DEEP NETWORKS |
d231627759 | Temporal networks serve as abstractions of many real-world dynamic systems. | INDUCTIVE REPRESENTATION LEARNING IN TEMPO- RAL NETWORKS VIA CAUSAL ANONYMOUS WALKS |
d252683227 | We present Gradient Gating (G 2 ), a novel framework for improving the performance of Graph Neural Networks (GNNs). Our framework is based on gating the output of GNN layers with a mechanism for multi-rate flow of message passing information across nodes of the underlying graph. Local gradients are harnessed to further modulate message passing updates. Our framework flexibly allows one to use any basic GNN layer as a wrapper around which the multi-rate gradient gating mechanism is built. We rigorously prove that G 2 alleviates the oversmoothing problem and allows the design of deep GNNs. Empirical results are presented to demonstrate that the proposed framework achieves state-of-the-art performance on a variety of graph learning tasks, including on large-scale heterophilic graphs. | Published as a conference paper at ICLR 2023 GRADIENT GATING FOR DEEP MULTI-RATE LEARNING ON GRAPHS |
d247446712 | Human decision making is well known to be imperfect and the ability to analyse such processes individually is crucial when attempting to aid or improve a decisionmaker's ability to perform a task, e.g. to alert them to potential biases or oversights on their part. To do so, it is necessary to develop interpretable representations of how agents make decisions and how this process changes over time as the agent learns online in reaction to the accrued experience. To then understand the decisionmaking processes underlying a set of observed trajectories, we cast the policy inference problem as the inverse to this online learning problem. By interpreting actions within a potential outcomes framework, we introduce a meaningful mapping based on agents choosing an action they believe to have the greatest treatment effect. We introduce a practical algorithm for retrospectively estimating such perceived effects, alongside the process through which agents update them, using a novel architecture built upon an expressive family of deep state-space models. Through application to the analysis of UNOS organ donation acceptance decisions, we demonstrate that our approach can bring valuable insights into the factors that govern decision processes and how they change over time. | INVERSE ONLINE LEARNING: UNDERSTANDING NON- STATIONARY AND REACTIONARY POLICIES |
d257255242 | In this paper, we present StrucTexTv2, an effective document image pre-training framework, by performing masked visual-textual prediction. It consists of two self-supervised pre-training tasks: masked image modeling and masked language modeling, based on text region-level image masking. The proposed method randomly masks some image regions according to the bounding box coordinates of text words. The objectives of our pre-training tasks are reconstructing the pixels of masked image regions and the corresponding masked tokens simultaneously. Hence the pre-trained encoder can capture more textual semantics in comparison to the masked image modeling that usually predicts the masked image patches. Compared to the masked multi-modal modeling methods for document image understanding that rely on both the image and text modalities, StrucTexTv2 models image-only input and potentially deals with more application scenarios free from OCR pre-processing. Extensive experiments on mainstream benchmarks of document image understanding demonstrate the effectiveness of StrucTexTv2. It achieves competitive or even new state-of-the-art performance in various downstream tasks such as image classification, layout analysis, table structure recognition, document OCR, and information extraction under the end-to-end scenario. † Equal contribution. Correspondence to: Chengquan Zhang<zhangchengquan@baidu.com>. | STRUCTEXTV2: MASKED VISUAL-TEXTUAL PREDIC- TION FOR DOCUMENT IMAGE PRE-TRAINING |
d16550910 | Matrix approximation is a common tool in machine learning for building accurate prediction models for recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of low-rank. We propose a new matrix approximation model where we assume instead that the matrix is only locally of low-rank, leading to a representation of the observed matrix as a weighted sum of low-rank matrices. We analyze the accuracy of the proposed local low-rank modeling. Our experiments show improvements of prediction accuracy in recommendation tasks. | Matrix Approximation under Local Low-Rank Assumption |
d801438 | Most machine learning classifiers, including deep neural networks, are vulnerable to adversarial examples. Such inputs are typically generated by adding small but purposeful modifications that lead to incorrect outputs while imperceptible to human eyes. The goal of this paper is not to introduce a single method, but to make theoretical steps towards fully understanding adversarial examples. By using concepts from topology, our theoretical analysis brings forth the key reasons why an adversarial example can fool a classifier (f 1 ) and adds its oracle (f 2 , like human eyes) in such analysis. By investigating the topological relationship between two (pseudo)metric spaces corresponding to predictor f 1 and oracle f 2 , we develop necessary and sufficient conditions that can determine if f 1 is always robust (strongrobust) against adversarial examples according to f 2 . Interestingly our theorems indicate that just one unnecessary feature can make f 1 not strong-robust, and the right feature representation learning is the key to getting a classifier that is both accurate and strong-robust. arXiv:1505.06556, 2015.Wei Liu and Sanjay Chawla. Mining adversarial patterns via regularized loss minimization. Machine learning, 81(1):69-83, 2010. | Workshop track -ICLR 2017 A THEORETICAL FRAMEWORK FOR ROBUSTNESS OF (DEEP) CLASSIFIERS AGAINST ADVERSARIAL EXAMPLES |
d251649222 | Reasoning is a fundamental problem for computers and deeply studied in Artificial Intelligence. In this paper, we specifically focus on answering multi-hop logical queries on Knowledge Graphs (KGs). This is a complicated task because, in real-world scenarios, the graphs tend to be large and incomplete. Most previous works have been unable to create models that accept full First-Order Logical (FOL) queries, which include negative queries, and have only been able to process a limited set of query structures. Additionally, most methods present logic operators that can only perform the logical operation they are made for. We introduce a set of models that use Neural Networks to create one-point vector embeddings to answer the queries. The versatility of neural networks allows the framework to handle FOL queries with Conjunction (∧), Disjunction (∨) and Negation (¬) operators. We demonstrate experimentally the performance of our model through extensive experimentation on well-known benchmarking datasets. Besides having more versatile operators, the models achieve a 10% relative increase over the best performing state of the art and more than 30% over the original method based on single-point vector embeddings. | Published as a conference paper at ICLR 2022 NEURAL METHODS FOR LOGICAL REASONING OVER KNOWLEDGE GRAPHS |
d21727726 | Generative Adversarial Nets (GANs) and Variational Auto-Encoders (VAEs) provide impressive image generations from Gaussian white noise, but the underlying mathematics are not well understood. We compute deep convolutional network generators by inverting a fixed embedding operator. Therefore, they do not require to be optimized with a discriminator or an encoder. The embedding is Lipschitz continuous to deformations so that generators transform linear interpolations between input white noise vectors into deformations between output images. This embedding is computed with a wavelet Scattering transform. Numerical experiments demonstrate that the resulting Scattering generators have similar properties as GANs or VAEs, without learning a discriminative network or an encoder. | Published as a conference paper at ICLR 2018 GENERATIVE NETWORKS AS INVERSE PROBLEMS WITH SCATTERING TRANSFORMS |
d237485233 | Meta-learning empowers artificial intelligence to increase its efficiency by learning how to learn. Unlocking this potential involves overcoming a challenging metaoptimisation problem. We propose an algorithm that tackles this problem by letting the meta-learner teach itself. The algorithm first bootstraps a target from the metalearner, then optimises the meta-learner by minimising the distance to that target under a chosen (pseudo-)metric. Focusing on meta-learning with gradients, we establish conditions that guarantee performance improvements and show that the metric can control meta-optimisation. Meanwhile, the bootstrapping mechanism can extend the effective meta-learning horizon without requiring backpropagation through all updates. We achieve a new state-of-the art for model-free agents on the Atari ALE benchmark and demonstrate that it yields both performance and efficiency gains in multi-task meta-learning. Finally, we explore how bootstrapping opens up new possibilities and find that it can meta-learn efficient exploration in an ε-greedy Q-learning agent-without backpropagating through the update rule.We argue that defining the meta-learner's objective directly in terms of the learner's objective-i.e. the performance after K update steps-creates two bottlenecks in meta-optimisation. The first bottleneck is curvature: the meta-objective is constrained to the same type of geometry as the learner; the second is myopia: the meta-objective is fundamentally limited to evaluating performance within the K-step horizon, but ignores future learning dynamics. Our goal is to design an algorithm that removes these.The algorithm relies on two main ideas. First, to mitigate myopia, we introduce the notion of bootstrapping a target from the meta-learner itself, a meta-bootstrap, that infuses information about learning dynamics in the objective. Second, to control curvature, we formulate the meta-objective in terms of minimising distance (or divergence) to the bootstrapped target, thereby controlling the meta-loss landscape. In this way, the meta-learner learns from its future self. This leads to a bootstrapping effect where improvements beget further improvements. We present a detailed formulation in Section 3; on a high level, as in previous works, we first unroll the meta-learned update rule for K steps to obtain the learner's new parameters. Whereas standard meta-objectives optimise the update rule with respect to (w.r.t.) the learner's performance under the new parameters, our proposed algorithm constructs the meta-objective in two steps: | Published as a conference paper at ICLR 2022 BOOTSTRAPPED META-LEARNING |
d244463196 | How to extract as much learning signal from each trajectory data has been a key problem in reinforcement learning (RL), where sample inefficiency has posed serious challenges for practical applications. Recent works have shown that using expressive policy function approximators and conditioning on future trajectory information -such as future states in hindsight experience replay (HER) or returnsto-go in Decision Transformer (DT) -enables efficient learning of multi-task policies, where at times online RL is fully replaced by offline behavioral cloning (BC), e.g. sequence modeling. We demonstrate that all these approaches are doing hindsight information matching (HIM) -training policies that can output the rest of trajectory that matches some statistics of future state information. We present Generalized Decision Transformer (GDT) for solving any HIM problem, and show how different choices for the feature function and the anti-causal aggregator not only recover DT as a special case, but also lead to novel Categorical DT (CDT) and Bi-directional DT (BDT) for matching different statistics of the future. For evaluating CDT and BDT, we define offline multi-task state-marginal matching (SMM) and imitation learning (IL) as two generic HIM problems, propose a Wasserstein distance loss as a metric for both, and empirically study them on MuJoCo continuous control benchmarks. Categorical DT, which simply replaces anti-causal summation with anti-causal binning in DT, enables arguably the first effective offline multi-task SMM algorithm that generalizes well to unseen (and even synthetic) multi-modal reward or state-feature distributions. Bi-directional DT, which uses an anti-causal second transformer as the aggregator, can learn to model any statistics of the future and outperforms DT variants in offline multi-task IL, i.e. one-shot IL. Our generalized formulations from HIM and GDT greatly expand the role of powerful sequence modeling architectures in modern RL. | Published as a conference paper at ICLR 2022 GENERALIZED DECISION TRANSFORMER FOR OFFLINE HINDSIGHT INFORMATION MATCHING |
d51678464 | Many theories of deep learning have shown that a deep network can require dramatically fewer resources to represent a given function compared to a shallow network. But a question remains: can these efficient representations be learned using current deep learning techniques? In this work, we test whether standard deep learning methods can in fact find the efficient representations posited by several theories of deep representation. Specifically, we train deep neural networks to learn two simple functions with known efficient solutions: the parity function and the fast Fourier transform. We find that using gradient-based optimization, a deep network does not learn the parity function, unless initialized very close to a hand-coded exact solution. We also find that a deep linear neural network does not learn the fast Fourier transform, even in the best-case scenario of infinite training data, unless the weights are initialized very close to the exact hand-coded solution. Our results suggest that not every element of the class of compositional functions can be learned efficiently by a deep network, and further restrictions are necessary to understand what functions are both efficiently representable and learnable. | Workshop track -ICLR 2018 ARE EFFICIENT DEEP REPRESENTATIONS LEARNABLE? |
d203837683 | Graph embedding techniques have been increasingly deployed in a multitude of different applications that involve learning on non-Euclidean data. However, existing graph embedding models either fail to incorporate node attribute information during training or suffer from node attribute noise, which compromises the accuracy. Moreover, very few of them scale to large graphs due to their high computational complexity and memory usage. In this paper we propose GraphZoom, a multi-level framework for improving both accuracy and scalability of unsupervised graph embedding algorithms. 1 GraphZoom first performs graph fusion to generate a new graph that effectively encodes the topology of the original graph and the node attribute information. This fused graph is then repeatedly coarsened into much smaller graphs by merging nodes with high spectral similarities. GraphZoom allows any existing embedding methods to be applied to the coarsened graph, before it progressively refine the embeddings obtained at the coarsest level to increasingly finer graphs. We have evaluated our approach on a number of popular graph datasets for both transductive and inductive tasks. Our experiments show that GraphZoom can substantially increase the classification accuracy and significantly accelerate the entire graph embedding process by up to 40.8×, when compared to the state-of-the-art unsupervised embedding methods. * Equal contributions 1 Source code of GraphZoom is freely available at: github.com/cornell-zhang/GraphZoom. | Published as a conference paper at ICLR 2020 GRAPHZOOM: A MULTI-LEVEL SPECTRAL APPROACH FOR ACCURATE AND SCALABLE GRAPH EMBEDDING |
d6628106 | We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions. The method is straightforward to implement and is based on adaptive estimates of lower-order moments of the gradients. The method is computationally efficient, has little memory requirements and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The method exhibits invariance to diagonal rescaling of the gradients by adapting to the geometry of the objective function. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. We demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. * Equal contribution. Author ordering determined by coin flip over a Google Hangout. | ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION |
d235606453 | Conventional representation learning algorithms for knowledge graphs (KG) map each entity to a unique embedding vector. Such a shallow lookup results in a linear growth of memory consumption for storing the embedding matrix and incurs high computational costs when working with real-world KGs. Drawing parallels with subword tokenization commonly used in NLP, we explore the landscape of more parameter-efficient node embedding strategies. To this end, we propose NodePiece, an anchor-based approach to learn a fixed-size entity vocabulary. In NodePiece, a vocabulary of subword/sub-entity units is constructed from anchor nodes in a graph with known relation types. Given such a fixed-size vocabulary, it is possible to bootstrap an encoding and embedding for any entity, including those unseen during training. Experiments show that NodePiece performs competitively in node classification, link prediction, and relation prediction tasks while retaining less than 10% of explicit nodes in a graph as anchors and often having 10x fewer parameters. To this end, we show that a NodePiece-enabled model outperforms existing shallow models on a large OGB WikiKG 2 graph having~70x fewer parameters 1 . 1 The code is available on GitHub: https://github.com/migalkin/NodePiece 2 We then concentrate on nodes as usually their size is orders of magnitude larger than that of edge types. | NODEPIECE: COMPOSITIONAL AND PARAMETER- EFFICIENT REPRESENTATIONS OF LARGE KNOWL- EDGE GRAPHS |
d257102992 | Human similarity judgments are a powerful supervision signal for machine learning applications based on techniques such as contrastive learning, information retrieval, and model alignment, but classical methods for collecting human similarity judgments are too expensive to be used at scale. Recent methods propose using pre-trained deep neural networks (DNNs) to approximate human similarity, but pre-trained DNNs may not be available for certain domains (e.g., medical images, low-resource languages) and their performance in approximating human similarity has not been extensively tested. We conducted an evaluation of 611 pre-trained models across three domains -images, audio, video -and found that there is a large gap in performance between human similarity judgments and pre-trained DNNs. To address this gap, we propose a new class of similarity approximation methods based on language. To collect the language data required by these new methods, we also developed and validated a novel adaptive tag collection pipeline. We find that our proposed language-based methods are significantly cheaper, in the number of human judgments, than classical methods, but still improve performance over the DNN-based methods. Finally, we also develop 'stacked' methods that combine language embeddings with DNN embeddings, and find that these consistently provide the best approximations for human similarity across all three of our modalities. Based on the results of this comprehensive study, we provide a concise guide for researchers interested in collecting or approximating human similarity data. To accompany this guide, we also release all of the similarity and language data, a total of 206,339 human judgments, that we collected in our experiments, along with a detailed breakdown of all modeling results. . wav2vec 2.0: A framework for self-supervised learning of speech representations. . data2vec: A general framework for self-supervised learning in speech, vision and language, 2022. Hangbo Bao, Li Dong, and Furu Wei. BEiT: BERT pre-training of image transformers. arXiv preprint arXiv:2106.08254, 2021. H Clark Barrett. Towards a cognitive science of the human: cross-cultural approaches and their urgency. Trends in Cognitive Sciences, 24(8):620-638, 2020. , et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901, 2020. William Brown. Some experimental results in the correlation of mental abilities 1. , et al. WavLM: Large-scale self-supervised pre-training for full stack speech processing. arXiv preprint arXiv:2110.13900, 2021. Alan S Cowen and Dacher Keltner. Self-report captures 27 distinct categories of emotion bridged by continuous gradients. , et al. Generative timbre spaces: regularizing variational auto-encoders with perceptual metrics. arXiv preprint arXiv:1805.08501, 2018. | Published as a conference paper at ICLR 2023 WORDS ARE ALL YOU NEED? LANGUAGE AS AN AP- PROXIMATION FOR HUMAN SIMILARITY JUDGMENTS |
d245124014 | The quantitative analysis of non-invasive electrophysiology signals from electroencephalography (EEG) and magnetoencephalography (MEG) boils down to the identification of temporal patterns such as evoked responses, transient bursts of neural oscillations but also blinks or heartbeats for data cleaning. Several works have shown that these patterns can be extracted efficiently in an unsupervised way, e.g., using Convolutional Dictionary Learning. This leads to an event-based description of the data. Given these events, a natural question is to estimate how their occurrences are modulated by certain cognitive tasks and experimental manipulations. To address it, we propose a point process approach. While point processes have been used in neuroscience in the past, in particular for single cell recordings (spike trains), techniques such as Convolutional Dictionary Learning make them amenable to human studies based on EEG/MEG signals. We develop a novel statistical point process model -called driven temporal point processes (DriPP) -where the intensity function of the point process model is linked to a set of point processes corresponding to stimulation events. We derive a fast and principled expectation-maximization (EM) algorithm to estimate the parameters of this model. Simulations reveal that model parameters can be identified from long enough signals. Results on standard MEG datasets demonstrate that our methodology reveals event-related neural responses -both evoked and inducedand isolates non-task-specific temporal patterns. | DRIPP: DRIVEN POINT PROCESSES TO MODEL STIM- ULI INDUCED PATTERNS IN M/EEG SIGNALS |
d209439545 | Semmelhack et al. (2014)have achieved high classification accuracy in distinguishing swim bouts of zebrafish using a Support Vector Machine (SVM). Convolutional Neural Networks (CNNs) have reached superior performance in various image recognition tasks over SVMs, but their learnt features are not immediately visible. Reaching better transparency helps to build trust in their classifications and makes learned features interpretable to experts. Using a recently developed technique called Deep Taylor Decomposition, we generated heatmaps to highlight input regions of high relevance for predictions. We find that our CNN makes predictions by analyzing the steadiness of the tail's trunk, which markedly differs from the manually extracted features used bySemmelhack et al. (2014). We further uncovered that the network paid attention to experimental artifacts. Removing these artifacts ensured the validity of predictions. After correction, our best CNN beats the SVM by 6.12%, achieving a classification accuracy of 96.32%. Our work thus demonstrates the utility of AI explainability for CNNs. | ANALYSIS OF VIDEO FEATURE LEARNING IN TWO- STREAM CNNS ON THE EXAMPLE OF ZEBRAFISH SWIM BOUT CLASSIFICATION |
d235293778 | Time series are often complex and rich in information but sparsely labeled and therefore challenging to model. In this paper, we propose a self-supervised framework for learning generalizable representations for non-stationary time series. Our approach, called Temporal Neighborhood Coding (TNC), takes advantage of the local smoothness of a signal's generative process to define neighborhoods in time with stationary properties. Using a debiased contrastive objective, our framework learns time series representations by ensuring that in the encoding space, the distribution of signals from within a neighborhood is distinguishable from the distribution of non-neighboring signals. Our motivation stems from the medical field, where the ability to model the dynamic nature of time series data is especially valuable for identifying, tracking, and predicting the underlying patients' latent states in settings where labeling data is practically impossible. We compare our method to recently developed unsupervised representation learning approaches and demonstrate superior performance on clustering and classification tasks for multiple datasets. * | Published as a conference paper at ICLR 2021 UNSUPERVISED REPRESENTATION LEARNING FOR TIME SERIES WITH TEMPORAL NEIGHBORHOOD CODING |
d56538189 | Dictionary Learning has proven to be a powerful tool for many image processing tasks, where atoms are typically defined on small image patches. As a drawback, the dictionary only encodes basic structures. In addition, this approach treats patches of different locations in one single set, which means a loss of information when features are well-aligned across signals. This is the case, for instance, in multi-trial magneto-or electroencephalography (M/EEG). Learning the dictionary on the entire signals could make use of the alignment and reveal higher-level features. In this case, however, small misalignments or phase variations of features would not be compensated for. In this paper, we propose an extension to the common dictionary learning framework to overcome these limitations by allowing atoms to adapt their position across signals. The method is validated on simulated and real neuroelectric data. | Jitter-Adaptive Dictionary Learning -Application to Multi-Trial Neuroelectric Signals |
d210157251 | In multi-agent systems, complex interacting behaviors arise due to the high correlations among agents. However, previous work on modeling multi-agent interactions from demonstrations is primarily constrained by assuming the independence among policies and their reward structures. In this paper, we cast the multi-agent interactions modeling problem into a multi-agent imitation learning framework with explicit modeling of correlated policies by approximating opponents' policies, which can recover agents' policies that can regenerate similar interactions. Consequently, we develop a Decentralized Adversarial Imitation Learning algorithm with Correlated policies (CoDAIL), which allows for decentralized training and execution. Various experiments demonstrate that CoDAIL can better regenerate complex interactions close to the demonstrators and outperforms state-of-theart multi-agent imitation learning methods. | MULTI-AGENT INTERACTIONS MODELING WITH COR- RELATED POLICIES |
d231951546 | Semi-supervised learning, i.e., training networks with both labeled and unlabeled data, has made significant progress recently. However, existing works have primarily focused on image classification tasks and neglected object detection which requires more annotation effort. In this work, we revisit the Semi-Supervised Object Detection (SS-OD) and identify the pseudo-labeling bias issue in SS-OD. To address this, we introduce Unbiased Teacher 1 , a simple yet effective approach that jointly trains a student and a gradually progressing teacher in a mutually-beneficial manner. Together with a class-balance loss to downweight overly confident pseudo-labels, Unbiased Teacher consistently improved state-ofthe-art methods by significant margins on COCO-standard, COCO-additional, and VOC datasets. Specifically, Unbiased Teacher achieves 6.8 absolute mAP improvements against state-of-the-art method when using 1% of labeled data on MS-COCO, achieves around 10 mAP improvements against the supervised baseline when using only 0.5, 1, 2% of labeled data on MS-COCO. | Published as a conference paper at ICLR 2021 UNBIASED TEACHER FOR SEMI-SUPERVISED OBJECT DETECTION |
d7777777 | This paper introduces a new architecture for human pose estimation using a multilayer convolutional network architecture and a modified learning technique that learns low-level features and a higher-level weak spatial model.Unconstrained human pose estimation is one of the hardest problems in computer vision, and our new architecture and learning schema shows improvement over the current stateof-the-art.The main contribution of this paper is showing, for the first time, that a specific variation of deep learning is able to meet the performance, and in many cases outperform, existing traditional architectures on this task.The paper also discusses several lessons learned while researching alternatives, most notably, that it is possible to learn strong low-level feature detectors on regions that might only cover a few pixels in the image.Higher-level spatial models improve somewhat the overall result, but to a much lesser extent than expected.Many researchers previously argued that the kinematic structure and top-down information are crucial for this domain, but with our purely bottom-up, and weak spatial model, we improve on other more complicated architectures that currently produce the best results.This echos what many other researchers, like those in the speech recognition, object recognition, and other domains have experienced[26]. | Learning Human Pose Estimation Features with Convolutional Networks |
d257687184 | Dropped into an unknown environment, what should an agent do to quickly learn about the environment and how to accomplish diverse tasks within it? We address this question within the goal-conditioned reinforcement learning paradigm, by identifying how the agent should set its goals at training time to maximize exploration. We propose "Planning Exploratory Goals" (PEG), a method that sets goals for each training episode to directly optimize an intrinsic exploration reward. PEG first chooses goal commands such that the agent's goal-conditioned policy, at its current level of training, will end up in states with high exploration potential. It then launches an exploration policy starting at those promising states. To enable this direct optimization, PEG learns world models and adapts sampling-based planning algorithms to "plan goal commands". In challenging simulated robotics environments including a multi-legged ant robot in a maze, and a robot arm on a cluttered tabletop, PEG exploration enables more efficient and effective training of goal-conditioned policies relative to baselines and ablations. Our ant successfully navigates a long maze, and the robot arm successfully builds a stack of three blocks upon command. Website: https://penn-pal-lab.github.io/peg/ . Visual foresight: Model-based deep reinforcement learning for vision-based robotic control. arXiv preprint arXiv:1812.00568, 2018. . Memory based trajectory-conditioned policies for learning from sparse rewards. . Towards practical multi-object manipulation using relational reinforcement learning. | Published as a conference paper at ICLR 2023 PLANNING GOALS FOR EXPLORATION |
d211171773 | Incorporating hierarchical structures like constituency trees has been shown to be effective for various natural language processing (NLP) tasks. However, it is evident that state-of-the-art (SOTA) sequence-based models like the Transformer struggle to encode such structures inherently. On the other hand, dedicated models like the Tree-LSTM, while explicitly modeling hierarchical structures, do not perform as efficiently as the Transformer. In this paper, we attempt to bridge this gap with "Hierarchical Accumulation" to encode parse tree structures into selfattention at constant time complexity. Our approach outperforms SOTA methods in four IWSLT translation tasks and the WMT'14 English-German translation task. It also yields improvements over Transformer and Tree-LSTM on three text classification tasks. We further demonstrate that using hierarchical priors can compensate for data shortage, and that our model prefers phrase-level attentions over token-level attentions.Published as a conference paper at ICLR 2020 are combined into a new value representation of the target node by using weighted aggregation. Finally, the model proceeds to perform attention with subtree masking where the attention score between a nonterminal query and a key is activated only if the key is a descendant of the query.Our contributions are threefold. First, we present our attention-based hierarchical encoding method. Our method overcomes linear parallel time complexity of Tree-LSTM(Tai et al., 2015)and offers attractive scalability. Second, we adopt our methods within the Transformer architecture and show improvements across various NLP tasks over strong baselines. In particular, our model leverages tree-based prior to improve translation quality over the Transformer baselines in the IWSLT'14 English-German and German-English, the IWSLT'13 English-French and French-English, and the WMT'14 English-German translation tasks. Furthermore, our model also exhibits advantages over Tree-LSTM in classification tasks including Stanford Sentiment Analysis (SST)(Socher et al., 2013), IMDB Sentiment Analysis and Subject-Verb Agreement(Linzen et al., 2016). Finally, our analysis of the results suggests that incorporating a hierarchical prior using our method can compensate for the lack of data in the context of machine translation. We also demonstrate that the model has natural and consistent preference for phrase-level attention over token-level attention. Our source code is available at https://github.com/nxphi47/tree transformer. | TREE-STRUCTURED ATTENTION WITH HIERARCHI- CAL ACCUMULATION |
d256826794 | Standard empirical risk minimization (ERM) training can produce deep neural network (DNN) models that are accurate on average but underperform in underrepresented population subgroups, especially when there are imbalanced group distributions in the long-tailed training data. Therefore, approaches that improve the accuracy -group robustness tradeoff frontier of a DNN model (i.e. improving worst-group accuracy without sacrificing average accuracy, or vice versa) is of crucial importance. Uncertainty-based active learning (AL) can potentially improve the frontier by preferentially sampling underrepresented subgroups to create a more balanced training dataset. However, the quality of uncertainty estimates from modern DNNs tend to degrade in the presence of spurious correlations and dataset bias, compromising the effectiveness of AL for sampling tail groups. In this work, we propose Introspective Self-play (ISP), a simple approach to improve the uncertainty estimation of a deep neural network under dataset bias, by adding an auxiliary introspection task requiring a model to predict the bias for each data point in addition to the label. We show that ISP provably improves the bias-awareness of the model representation and the resulting uncertainty estimates. On two realworld tabular and language tasks, ISP serves as a simple "plug-in" for AL model training, consistently improving both the tail-group sampling rate and the final accuracy-fairness trade-off frontier of popular AL methods. * Co-sernior authors. † Work done as a student researcher at Google. MS contributed to the manuscript after its initial ICLR submission. . A simple approach to improve single-model deep uncertainty via distance-awareness. arXiv preprint arXiv:2205.00403, 2022.Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda. Early-learning regularization prevents memorization of noisy labels. Advances in neural information processing systems, , et al. Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140):1-67, 2020.Ruggero Ragonesi, Riccardo Volpi, Jacopo Cavazza, and Vittorio Murino. Learning unbiased representations via mutual information backpropagation. In | PUSHING THE ACCURACY-GROUP ROBUSTNESS FRON- TIER WITH INTROSPECTIVE SELF-PLAY |
d196831582 | An open secret in contemporary machine learning is that many models work beautifully on standard benchmarks but fail to generalize outside the lab. This has been attributed to biased training data, which provide poor coverage over real world events. Generative models are no exception, but recent advances in generative adversarial networks (GANs) suggest otherwise -these models can now synthesize strikingly realistic and diverse images. Is generative modeling of photos a solved problem? We show that although current GANs can fit standard datasets very well, they still fall short of being comprehensive models of the visual manifold. In particular, we study their ability to fit simple transformations such as camera movements and color changes. We find that the models reflect the biases of the datasets on which they are trained (e.g., centered objects), but that they also exhibit some capacity for generalization: by "steering" in latent space, we can shift the distribution while still creating realistic images. We hypothesize that the degree of distributional shift is related to the breadth of the training data distribution. Thus, we conduct experiments to quantify the limits of GAN transformations and introduce techniques to mitigate the problem. Code is released on our project page: | Published as a conference paper at ICLR 2020 ON THE "STEERABILITY" OF GENERATIVE ADVERSARIAL NETWORKS |
d16636683 | We propose a novel deep network structure called "Network In Network"(NIN) to enhance model discriminability for local patches within the receptive field. The conventional convolutional layer uses linear filters followed by a nonlinear activation function to scan the input. Instead, we build micro neural networks with more complex structures to abstract the data within the receptive field. We instantiate the micro neural network with a multilayer perceptron, which is a potent function approximator. The feature maps are obtained by sliding the micro networks over the input in a similar manner as CNN; they are then fed into the next layer. Deep NIN can be implemented by stacking mutiple of the above described structure. With enhanced local modeling via the micro network, we are able to utilize global average pooling over feature maps in the classification layer, which is easier to interpret and less prone to overfitting than traditional fully connected layers. We demonstrated the state-of-the-art classification performances with NIN on CIFAR-10 and CIFAR-100, and reasonable performances on SVHN and MNIST datasets. | Network In Network |
d257280093 | Scaling transformers has led to significant breakthroughs in many domains, leading to a paradigm in which larger versions of existing models are trained and released on a periodic basis. New instances of such models are typically trained completely from scratch, despite the fact that they are often just scaled-up versions of their smaller counterparts. How can we use the implicit knowledge in the parameters of smaller, extant models to enable faster training of newer, larger models? This paper describes an approach for accelerating transformer training by learning to grow pretrained transformers, where we learn to linearly map the parameters of the smaller model to initialize the larger model. For tractable learning, we factorize the linear transformation as a composition of (linear) widthand depth-growth operators, and further employ a Kronecker factorization of these growth operators to encode architectural knowledge. Extensive experiments across both language and vision transformers demonstrate that our learned Linear Growth Operator (LiGO) can save up to 50% computational cost of training from scratch, while also consistently outperforming strong baselines that also reuse smaller pretrained models to initialize larger models. 1 * Work done during an internship at MIT-IBM Watson AI Lab. | Published as a conference paper at ICLR 2023 LEARNING TO GROW PRETRAINED MODELS FOR EFFICIENT TRANSFORMER TRAINING |
d2187805 | Probabilistic generative models can be used for compression, denoising, inpainting, texture synthesis, semi-supervised learning, unsupervised feature learning, and other tasks. Given this wide range of applications, it is not surprising that a lot of heterogeneity exists in the way these models are formulated, trained, and evaluated. As a consequence, direct comparison between models is often difficult. This article reviews mostly known but often underappreciated properties relating to the evaluation and interpretation of generative models with a focus on image models. In particular, we show that three of the currently most commonly used criteria-average log-likelihood, Parzen window estimates, and visual fidelity of samples-are largely independent of each other when the data is high-dimensional. Good performance with respect to one criterion therefore need not imply good performance with respect to the other criteria. Our results show that extrapolation from one criterion to another is not warranted and generative models need to be evaluated directly with respect to the application(s) they were intended for. In addition, we provide examples demonstrating that Parzen window estimates should generally be avoided. | A NOTE ON THE EVALUATION OF GENERATIVE MODELS |
d252693237 | Large language models (LLMs) have shown remarkable reasoning capabilities given chain-of-thought prompts (examples with intermediate reasoning steps). Existing benchmarks measure reasoning ability indirectly, by evaluating accuracy on downstream tasks such as mathematical reasoning. However, it is unclear how these models obtain the answers and whether they rely on simple heuristics rather than the generated chain-of-thought. To enable systematic exploration of the reasoning ability of LLMs, we present a new synthetic question-answering dataset called PRONTOQA, where each example is generated from a synthetic world model represented in first-order logic. This allows us to parse the generated chain-ofthought into symbolic proofs for formal analysis. Our analysis on INSTRUCTGPT and GPT-3 shows that LLMs are quite capable of making correct individual deduction steps, and so are generally capable of reasoning, even in fictional contexts. However, they have difficulty with proof planning: When multiple valid deduction steps are available, they are not able to systematically explore the different options.Published as a conference paper at ICLR 2023 Q: Each cat is a carnivore. Every carnivore is not herbivorous. Carnivores are mammals. All mammals are warm-blooded. Mammals are vertebrates. Every vertebrate is an animal. Animals are multicellular. Fae is a cat. True or false: Fae is not herbivorous. A: Fae is a cat. Cats are carnivores. Fae is a carnivore. Every carnivore is not herbivorous. Fae is not herbivorous. True context query chain-of-thought label FIGURE 1: A question-answering example from PRONTOQA, with each component highlighted and labeled. animal multicellular vertebrate mammal warm_blooded carnivore ¬herbivorous catStep 1: Generate ontologyStep 2: Generate proof from ontologyHop ¬herbivorous(fae)Step 3: Translate ontology to natural language context "Q: Each cat is a carnivore. Every carnivore is not herbivorous. Carnivores are mammals. All mammals are warm-blooded. Mammals are vertebrates. Every vertebrate is an animal. Animals are multicellular."Step 4: Translate proof into query, chain-of-thought, and label "Fae is a cat. True or false: Fae is not herbivorous. A: Fae is a cat. Cats are carnivores. Fae is a carnivore. Every carnivore is not herbivorous. Fae is not herbivorous. True" FIGURE 2: Schematic of the generative process for each example in PRONTOQA.Step 1: We generate an ontology from a prior distribution, shown here as a tree. Each node denotes a concept (e.g., mammal), each with an optional property (e.g., warm_blooded), and each blue edge denotes a "subtype of" relation.Step 2: Generate proof from the ontology. Each horizontal black line indicates a proof step, with its premises written above the line and the conclusion written below.Step 3: Convert the ontology into a natural language context. Step 4: Convert the proof into a natural language query, chain-of-thought, and answer label. There is a one-to-one correspondence between the conclusion of each proof step and the sentences in the chain-of-thought. | LANGUAGE MODELS ARE GREEDY REASONERS: A SYSTEMATIC FORMAL ANALYSIS OF CHAIN-OF- THOUGHT |
d247958276 | We present Reward-Switching Policy Optimization (RSPO), a paradigm to discover diverse strategies in complex RL environments by iteratively finding novel policies that are both locally optimal and sufficiently different from existing ones. To encourage the learning policy to consistently converge towards a previously undiscovered local optimum, RSPO switches between extrinsic and intrinsic rewards via a trajectory-based novelty measurement during the optimization process. When a sampled trajectory is sufficiently distinct, RSPO performs standard policy optimization with extrinsic rewards. For trajectories with high likelihood under existing policies, RSPO utilizes an intrinsic diversity reward to promote exploration. Experiments show that RSPO is able to discover a wide spectrum of strategies in a variety of domains, ranging from single-agent particle-world tasks and MuJoCo continuous control to multi-agent stag-hunt games and StarCraftII challenges. -rl: Efficient mixing of quality and diversity in reinforcement learning. arXiv preprint arXiv: | Published as a conference paper at ICLR 2022 CONTINUOUSLY DISCOVERING NOVEL STRATEGIES VIA REWARD-SWITCHING POLICY OPTIMIZATION |
d229156351 | Recently, sequence-to-sequence (seq2seq) models with the Transformer architecture have achieved remarkable performance on various conditional text generation tasks, such as machine translation. However, most of them are trained with teacher forcing with the ground truth label given at each time step, without being exposed to incorrectly generated tokens during training, which hurts its generalization to unseen inputs, that is known as the "exposure bias" problem. In this work, we propose to mitigate the conditional text generation problem by contrasting positive pairs with negative pairs, such that the model is exposed to various valid or incorrect perturbations of the inputs, for improved generalization. However, training the model with naïve contrastive learning framework using random non-target sequences as negative examples is suboptimal, since they are easily distinguishable from the correct output, especially so with models pretrained with large text corpora. Also, generating positive examples requires domain-specific augmentation heuristics which may not generalize over diverse domains. To tackle this problem, we propose a principled method to generate positive and negative samples for contrastive learning of seq2seq models. Specifically, we generate negative examples by adding small perturbations to the input sequence to minimize its conditional likelihood, and positive examples by adding large perturbations while enforcing it to have a high conditional likelihood. Such "hard" positive and negative pairs generated using our method guides the model to better distinguish correct outputs from incorrect ones. We empirically show that our proposed method significantly improves the generalization of the seq2seq on three text Negative Imposter He wasn't in good shape.GT Target SentenceDistant-Target PerturbationHe was was in good shape.PerturbationSource SentenceEncoder-DecoderHe wasn't in great shape <eos> <bos> He wasn't in great shapeSource SentenceEncoder-DecoderHe wasn't in great shape <eos> <bos> He wasn't in great shapeManifold Manifold(a) Teacher ForcingGT Target SentenceSource SentenceEncoder-DecoderHe wasn't in great shape <eos> <bos> He wasn't in great shape Manifold RO: De partea cealaltă, 47% dintre alegătorii republicani afirmă că ar fi "nemulțumiți" sau "supărați" dacă favoritul Jeb Bush câștigă cursa pentru nominalizare. | CONTRASTIVE LEARNING WITH ADVERSARIAL PER- TURBATIONS FOR CONDITIONAL TEXT GENERATION |
d220703030 | As increasingly complex AI systems are introduced into our daily lives, it becomes important for such systems to be capable of explaining the rationale for their decisions and allowing users to contest these decisions. A significant hurdle to allowing for such explanatory dialogue could be the vocabulary mismatch between the user and the AI system. This paper introduces methods for providing contrastive explanations in terms of user-specified concepts for sequential decision-making settings where the system's model of the task may be best represented as an inscrutable model. We do this by building partial symbolic models of a local approximation of the task that can be leveraged to answer the user queries. We test these methods on a popular Atari game (Montezuma's Revenge) and variants of Sokoban (a well-known planning benchmark) and report the results of user studies to evaluate whether people find explanations generated in this form useful. . Learning by experimentation: The operator refinement method. In Machine learning, pp. 191-213. Elsevier, 1990. | Published as a conference paper at ICLR 2022 BRIDGING THE GAP: PROVIDING POST-HOC SYMBOLIC EXPLANATIONS FOR SEQUENTIAL DECISION-MAKING PROBLEMS WITH INSCRUTABLE REPRESENTATIONS |
d159386152 | ments and practices of the old constitution and tradition?Because the source of Islamic Shariah, the foundation of the Aceh community life is articulated in the modern perspective of democratic and responsible state government.Basic aspirations of the specificity of Acehnese religious community life not only in the field of custom, cultural, social and political, but provide legal certainty in all affairs.The main target responds to the failure of state challenges to uphold the law, democracy, freedom of human rights and justice.The study was limited to how their wishes to change the concept of legal arrangements and practices worked in the real world, linking the legal unity with the facts of society to the two sets of legal documents governing Aceh.The study used normative approach, legal history, and comparative law.Juridically, there are two main goals to be achieved from the implementation of the arrangements and practices in this study.First, it places the differences and equations of both models of the source of local wisdom law and the design of state law.Secondly, it turns out that the concept of Acehnese legal culture highly values pluralism. | Model of Local Wisdom Legal Source and State Law in Aceh Government ZULFAN |
d7047554 | Scalability properties of deep neural networks raise key research questions, particularly as the problems considered become larger and more challenging. This paper expands on the idea of conditional computation introduced in [2], where the nodes of a deep network are augmented by a set of gating units that determine when a node should be calculated. By factorizing the weight matrix into a low-rank approximation, an estimation of the sign of the pre-nonlinearity activation can be efficiently obtained. For networks using rectified-linear hidden units, this implies that the computation of a hidden unit with an estimated negative pre-nonlinearity can be omitted altogether, as its value will become zero when nonlinearity is applied. For sparse neural networks, this can result in considerable speed gains. Experimental results using the MNIST and SVHN data sets with a fully-connected deep neural network demonstrate the performance robustness of the proposed scheme with respect to the error introduced by the conditional computation process. | Low-Rank Approximations for Conditional Feedforward Computation in Deep Neural Networks |
d6587686 | People can learn a wide range of tasks from their own experience, but can also learn from observing other creatures. This can accelerate acquisition of new skills even when the observed agent differs substantially from the learning agent in terms of morphology. In this paper, we examine how reinforcement learning algorithms can transfer knowledge between morphologically different agents (e.g., different robots). We introduce a problem formulation where two agents are tasked with learning multiple skills by sharing information. Our method uses the skills that were learned by both agents to train invariant feature spaces that can then be used to transfer other skills from one agent to another. The process of learning these invariant feature spaces can be viewed as a kind of "analogy making," or implicit learning of partial correspondences between two distinct domains. We evaluate our transfer learning algorithm in two simulated robotic manipulation skills, and illustrate that we can transfer knowledge between simulated robotic arms with different numbers of links, as well as simulated arms with different actuation mechanisms, where one robot is torque-driven while the other is tendon-driven. * These authors contributed equally to this work. | LEARNING INVARIANT FEATURE SPACES TO TRANS- FER SKILLS WITH REINFORCEMENT LEARNING |
d16561904 | Reinforcement learning optimizes policies for expected cumulative reward. Need the supervision be so narrow? Reward is delayed and sparse for many tasks, making it a difficult and impoverished signal for end-to-end optimization. To augment reward, we consider a range of selfsupervised tasks that incorporate states, actions, and successors to provide auxiliary losses. These losses offer ubiquitous and instantaneous supervision for representation learning even in the absence of reward. While current results show that learning from reward alone is feasible, pure reinforcement learning methods are constrained by computational and data efficiency issues that can be remedied by auxiliary losses. Self-supervised pre-training and joint optimization improve the data efficiency and policy returns of end-to-end reinforcement learning. | Loss is its own Reward: Self-Supervision for Reinforcement Learning |
d256389841 | Modern studies in radiograph representation learning (R 2 L) rely on either selfsupervision to encode invariant semantics or associated radiology reports to incorporate medical expertise, while the complementarity between them is barely noticed. To explore this, we formulate the self-and report-completion as two complementary objectives and present a unified framework based on masked record modeling (MRM). In practice, MRM reconstructs masked image patches and masked report tokens following a multi-task scheme to learn knowledge-enhanced semantic representations. With MRM pre-training, we obtain pre-trained models that can be well transferred to various radiography tasks. Specifically, we find that MRM offers superior performance in label-efficient fine-tuning. For instance, MRM achieves 88.5% mean AUC on CheXpert using 1% labeled data, outperforming previous R 2 L methods with 100% labels. On NIH ChestX-ray, MRM outperforms the best performing counterpart by about 3% under small labeling ratios. Besides, MRM surpasses self-and report-supervised pre-training in identifying the pneumonia type and the pneumothorax area, sometimes by large margins. Code and models are available at https://github.comPublished as a conference paper at ICLR 2023 these labelers have several practical limitations. First, some procedures of the label extraction workflow, such as rulemaking and natural language processing, still require the intensive involvement of experts and engineers. Besides, the developed labelers can hardly adapt to new scenarios due to the fixed rules and lexicons.Against this background, report-supervised R 2 L was proposed(Zhang et al., 2020)to acquire supervision from radiology reports. In practice, this paradigm leverages words and sentences in free-text reports as supervision to guide deep neural networks to learn radiograph representations, outperforming the archetypical label-and self-supervised pre-training by observable margins in various downstream tasks(Zhang et al., 2020;Zhou et al., 2022). The report-supervised R 2 L highlights the importance of the incorporation of domain knowledge. This differs from the self-supervised paradigm, which focuses on learning invariant semantic representations. Nonetheless, current studies view the self-and report-supervised R 2 L as separate, discrete choices, preventing their combinations.Driven by this analysis, we present a unified framework based on masked record modeling (MRM), where the self-and report-completion tasks are modeled as two complementary objectives. Specifically, masked image reconstruction integrates semantics into pre-trained models, while masked report restoration facilitates the incorporation of medical expertise. As a result, MRM learns knowledge-enhanced semantic representations that generalize well. In practice, MRM masks random patches and tokens from the input radiograph and associated radiology report with high masking ratios. Following a multi-task scheme, MRM asks the radiography pre-trained model to learn visual representations that can not only reconstruct the missing patches but also restore the missing tokens from the non-masked token embeddings along with mask tokens.With MRM pre-training, we can train radiography models on MIMIC-CXR (Johnson et al., 2019) with improved generalization performance. With a pre-trained ViT-B/16 model, we achieve 88.5% mean AUC when fine-tuned on CheXpert (Irvin et al., 2019) with only 1% labels. This outperforms all previous counterparts with 100% labeled data. On NIH ChestX-ray(Wang & Gupta, 2015), MRM surpasses the report-supervised paradigm by about 3% when the labeling ratios 1 are 1% and 10%. On pneumonia identification tasks, MRM outperforms self-and report-supervised baselines, sometimes by substantial margins. These observations help verify the effectiveness of MRM in learning more transferable radiograph representations. | Published as a conference paper at ICLR 2023 ADVANCING RADIOGRAPH REPRESENTATION LEARN- ING WITH MASKED RECORD MODELING |
d57375723 | End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear close together in a single document. In this work, we propose the Coarse-grain Fine-grain Coattention Network (CFC), a new question answering model that combines information from evidence across multiple documents. The CFC consists of a coarse-grain module that interprets documents with respect to the query then finds a relevant answer, and a fine-grain module which scores each candidate answer by comparing its occurrences across all of the documents with the query. We design these modules using hierarchies of coattention and selfattention, which learn to emphasize different parts of the input. On the Qangaroo WikiHop multi-evidence question answering task, the CFC obtains a new stateof-the-art result of 70.6% on the blind test set, outperforming the previous best by 3% accuracy despite not using pretrained contextual encoders. Figure 1: The Coarse-grain Fine-grain Coattention Network.outputs from a traditional span extraction model (Clark & Gardner, 2018) using the CFC improves exact match accuracy by 3.1% and F1 by 3.0%.Our analysis shows that components in the attention hierarchies of the coarse and fine-grain modules learn to focus on distinct parts of the input. This enables the CFC to more effectively represent a large collection of long documents. Finally, we outline common types of errors produced by CFC, caused by difficulty in aggregating large quantity of references, noise in distant supervision, and difficult relation types.COARSE-GRAIN FINE-GRAIN COATTENTION NETWORKThe coarse-grain module and fine-grain module of the CFC correspond to coarse-grain reasoning and fine-grain reasoning strategies. The coarse-grain module summarizes support documents without knowing the candidates: it builds codependent representations of support documents and the query using coattention, then produces a coarse-grain summary using self-attention. In contrast, the fine-grain module retrieves specific contexts in which each candidate occurs: it identifies coreferent mentions of the candidate, then uses coattention to build codependent representations between these mentions and the query. While low-level encodings of the inputs are shared between modules, we show that this division of labour allows the attention hierarchies in each module to focus on different parts of the input. This enables the model to more effectively represent a large number of potentially long support documents.Suppose we are given a query, a set of N s support documents, and a set of N c candidates. Without loss of generality, let us consider the ith document and the jth candidate. Let L q ∈ R Tq×d emb , L s ∈ R Ts×d emb , and L c ∈ R Tc×d emb respectively denote the word embeddings of the query, the ith support document, and the jth candidate answer. Here, T q , T s , and T c are the number of words in the corresponding sequence. d emb is the size of the word embedding. We begin by encoding each sequence using a bidirectional Gated Recurrent Units (GRUs) (Cho et al., 2014).Nicola De Cao, Wilker Aziz, and Ivan Titov. Question answering by reasoning across documents with graph convolutional networks. arXiv preprint arXiv:1808.09920, 2018.SangKeun Lee Deunsol Yoon, Dongbok Lee. Dynamic self-attention: Computing attention over words dynamically for sentence embedding. arXiv preprint arXiv:1808.07383, 2018. models for reasoning over multiple mentions using coreference. arXiv preprint arXiv:1804.05922, 2018.Li Dong and Mirella Lapata. Coarse-to-fine decoding for neural semantic parsing. In ACL, 2018.Surabhi Gupta, Ani Nenkova, and Dan Jurafsky. Measuring importance and query relevance in topic-focused multi-document summarization. In ACL, 2007. | COARSE-GRAIN FINE-GRAIN COATTENTION NET- WORK FOR MULTI-EVIDENCE QUESTION ANSWERING |
d14809601 | We propose a randomized relax-and-round inference algorithm that samples near-MAP configurations of a binary pairwise Markov random field. We experiment on MAP inference tasks in several restricted Boltzmann machines. We also use our underlying sampler to estimate the log-partition function of restricted Boltzmann machines and compare against other sampling-based methods. | Relaxations for inference in restricted Boltzmann machines |
d257353585 | widely used in commercial acoustic systems in recent years.However, adversarial audio examples can cause abnormal behaviors for those acoustic systems, while being hard for humans to perceive.Various methods, such as transformation-based defenses and adversarial training, have been proposed to protect acoustic systems from adversarial attacks, but they are less effective against adaptive attacks.Furthermore, directly applying the methods from the image domain can lead to suboptimal results because of the unique properties of audio data.In this paper, we propose an adversarial purification-based defense pipeline, AudioPure, for acoustic systems via offthe-shelf diffusion models.Taking advantage of the strong generation ability of diffusion models, AudioPure first adds a small amount of noise to the adversarial audio and then runs the reverse sampling step to purify the noisy audio and recover clean audio.AudioPure is a plug-and-play method that can be directly applied to any pretrained classifier without any fine-tuning or re-training.We conduct extensive experiments on speech command recognition task to evaluate the robustness of AudioPure.Our method is effective against diverse adversarial attacks (e.g.L 2 or L ∞ -norm).It outperforms the existing methods under both strong adaptive white-box and black-box attacks bounded by L 2 or L ∞norm (up to +20% in robust accuracy).Besides, we also evaluate the certified robustness for perturbations bounded by L 2 -norm via randomized smoothing.Our pipeline achieves a higher certified accuracy than baselines.Code is available at https://github.com/cychomatica/AudioPure.
| DEFENDING AGAINST ADVERSARIAL AUDIO VIA DIFFUSION MODEL
2 Mar 2023
Shutong Wu
Arizona State Univer |
d8393918 | Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases. In this work, we study feature learning techniques for graph-structured inputs. Our starting point is previous work on Graph Neural Networks(Scarselli et al., 2009), which we modify to use gated recurrent units and modern optimization techniques and then extend to output sequences. The result is a flexible and broadly useful class of neural network models that has favorable inductive biases relative to purely sequence-based models (e.g., LSTMs) when the problem is graph-structured. We demonstrate the capabilities on some simple AI (bAbI) and graph algorithm learning tasks. We then show it achieves state-of-the-art performance on a problem from program verification, in which subgraphs need to be described as abstract data structures.Published as a conference paper at ICLR 2016 to learn features on the graph that encode the partial output sequence that has already been produced (e.g., the path so far if outputting a path) and that still needs to be produced (e.g., the remaining path). We will show how the GNN framework can be adapted to these settings, leading to a novel graph-based neural network model that we call Gated Graph Sequence Neural Networks (GGS-NNs).We illustrate aspects of this general model in experiments on bAbI tasks and graph algorithm learning tasks that illustrate the capabilities of the model. We then present an application to the verification of computer programs. When attempting to prove properties such as memory safety (i.e., that there are no null pointer dereferences in a program), a core problem is to find mathematical descriptions of the data structures used in a program. FollowingBrockschmidt et al. (2015), we have phrased this as a machine learning problem where we will learn to map from a set of input graphs, representing the state of memory, to a logical description of the data structures that have been instantiated. Whereas Brockschmidt et al.(2015)relied on a large amount of hand-engineering of features, we show that the system can be replaced with a GGS-NN at no cost in accuracy. | Published as a conference paper at ICLR 2016 GATED GRAPH SEQUENCE NEURAL NETWORKS |
d257353428 | Sparse Neural Networks (SNNs) have received voluminous attention predominantly due to growing computational and memory footprints of consistently exploding parameter count in large-scale models. Similar to their dense counterparts, recent SNNs generalize just as well and are equipped with numerous favorable benefits (e.g., low complexity, high scalability, and robustness), sometimes even better than the original dense networks. As research effort is focused on developing increasingly sophisticated sparse algorithms, it is startling that a comprehensive benchmark to evaluate the effectiveness of these algorithms has been highly overlooked. In absence of a carefully crafted evaluation benchmark, most if not all, sparse algorithms are evaluated against fairly simple and naive tasks (eg. CIFAR-10/100, ImageNet, GLUE, etc.), which can potentially camouflage many advantages as well unexpected predicaments of SNNs. In pursuit of a more general evaluation and unveiling the true potential of sparse algorithms, we introduce "Sparsity May Cry" Benchmark (SMC-Bench), a collection of carefully-curated 4 diverse tasks with 10 datasets, that accounts for capturing a wide range of domain-specific and sophisticated knowledge. Our systemic evaluation of the most representative sparse algorithms reveals an important obscured observation: the state-of-the-art magnitude-and/or gradient-based sparse algorithms seemingly fail to perform on SMC-Bench when applied out-of-the-box, sometimes at significantly trivial sparsity as low as 5%. The observations seek the immediate attention of the sparsity research community to reconsider the highly proclaimed benefits of SNNs. We further conduct a thorough investigation into the reasons for the failure of common SNNs. Our analysis points out that such failure is intimately related to the "lazy regime" of large model training, which hints us with stronger pruning recipes that alleviate the failure on SMC-Bench (though still more or less suffering). By incorporating these well-thought and diverse tasks, SMC-Bench is designed to favor and encourage the development of more scalable and generalizable sparse algorithms. We open-source SMC-Bench to assist researchers in building next-generation sparse algorithms that scale and generalize: https://github.com/VITA-Group/SMC-Bench. . Hierarchical textconditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 2022. | Published as a conference paper at ICLR 2023 SPARSITY MAY CRY: LET US FAIL (CURRENT) SPARSE NEURAL NETWORKS TOGETHER! |
d209478429 | Attribution methods provide insights into the decision-making of machine learning models like artificial neural networks. For a given input sample, they assign a relevance score to each individual input variable, such as the pixels of an image. In this work we adapt the information bottleneck concept for attribution. By adding noise to intermediate feature maps we restrict the flow of information and can quantify (in bits) how much information image regions provide. We compare our method against ten baselines using three different metrics on VGG-16 and ResNet-50, and find that our methods outperform all baselines in five out of six settings. The method's information-theoretic foundation provides an absolute frame of reference for attribution values (bits) and a guarantee that regions scored close to zero are not necessary for the network's decision.Published as a conference paper at ICLR 2020We propose a novel attribution method that estimates the amount of information an image region provides for the network's decision. We use a variational approximation to upper-bound this estimate and therefore can guarantee that areas with zero bits of information are not used.Figure 1shows an exemplary heatmap of our method. Up to 3 bits per pixel are available for regions corresponding to the monkeys' faces, whereas the tree is scored with close to zero bits per pixel. We can thus guarantee that the tree is not necessary for predicting the correct class, a guarantee, to the best of our knowledge, no other method can provide.To estimate the amount of information, we adapt the information bottleneck concept(Tishby et al., 2000;Alemi et al., 2017). The bottleneck is inserted into an existing neural network and restricts the information flow by adding noise to the activation maps. Unimportant activations are replaced almost entirely by noise, removing all information for subsequent network layers. We developed two approaches to learn the parameters of the bottleneck -either using a single sample (Per-Sample Bottleneck), or the entire dataset (Readout Bottleneck).We evaluate against ten different baselines. First, we calculated the Sensitivity-n metric proposed byAncona et al. (2018). Secondly, we quantified how well the object of interest was localized using bounding boxes and extend the degradation task proposed byAncona et al. (2017). In all these metrics our method outperforms the baselines consistently. Additionaly, we test the impact of cascading layer-wise weight randomizations on the attribution heatmaps(Adebayo et al., 2018). For reproducibility, we share our source code * . * https://github.com/attribution-bottleneck/attribution-bottleneck-pytorch | Published as a conference paper at ICLR 2020 RESTRICTING THE FLOW: INFORMATION BOTTLE- NECKS FOR ATTRIBUTION |
d14249389 | For image recognition and labeling tasks, recent results suggest that machine learning methods that rely on manually specified feature representations may be outperformed by methods that automatically derive feature representations based on the data. Yet for problems that involve analysis of 3d objects, such as mesh segmentation, shape retrieval, or neuron fragment agglomeration, there remains a strong reliance on hand-designed feature descriptors. In this paper, we evaluate a large set of hand-designed 3d feature descriptors alongside features learned from the raw data using both end-to-end and unsupervised learning techniques, in the context of agglomeration of 3d neuron fragments. By combining unsupervised learning techniques with a novel dynamic pooling scheme, we show how pure learning-based methods are for the first time competitive with hand-designed 3d shape descriptors. We investigate data augmentation strategies for dramatically increasing the size of the training set, and show how combining both learned and hand-designed features leads to the highest accuracy. | Learned versus Hand-Designed Feature Representations for 3d Agglomeration |
d209531600 | We propose an algorithm combining calibrated prediction and generalization bounds from learning theory to construct confidence sets for deep neural networks with PAC guarantees-i.e., the confidence set for a given input contains the true label with high probability. We demonstrate how our approach can be used to construct PAC confidence sets on ResNet for ImageNet, a visual object tracking model, and a dynamics model for the half-cheetah reinforcement learning problem. 1 1 Our code is available at https://github.com/sangdon/PAC-confidence-set. . Safe model-based reinforcement learning with stability guarantees. In Advances in neural information processing systems, pp. 908-918, 2017.Marko Bohanec and Vladislav Rajkovic. Knowledge acquisition and explanation for multi-attribute decision making. | PAC CONFIDENCE SETS FOR DEEP NEURAL NET- WORKS VIA CALIBRATED PREDICTION |
d235376961 | We present implicit displacement fields, a novel representation for detailed 3D geometry. Inspired by a classic surface deformation technique, displacement mapping, our method represents a complex surface as a smooth base surface plus a displacement along the base's normal directions, resulting in a frequency-based shape decomposition, where the high-frequency signal is constrained geometrically by the low-frequency signal. Importantly, this disentanglement is unsupervised thanks to a tailored architectural design that has an innate frequency hierarchy by construction. We explore implicit displacement field surface reconstruction and detail transfer and demonstrate superior representational power, training stability, and generalizability. Code and data available at: | Published as a conference paper at ICLR 2022 GEOMETRY-CONSISTENT NEURAL SHAPE REPRESENTATION WITH IMPLICIT DISPLACEMENT FIELDS |
d257232602 | Semi-supervised learning aims to train a model using limited labels. State-of-theart semi-supervised methods for image classification such as PAWS rely on selfsupervised representations learned with large-scale unlabeled but curated data. However, PAWS is often less effective when using real-world unlabeled data that is uncurated, e.g., contains out-of-class data. We propose RoPAWS, a robust extension of PAWS that can work with real-world unlabeled data. We first reinterpret PAWS as a generative classifier that models densities using kernel density estimation. From this probabilistic perspective, we calibrate its prediction based on the densities of labeled and unlabeled data, which leads to a simple closed-form solution from the Bayes' rule. We demonstrate that RoPAWS significantly improves PAWS for uncurated Semi-iNat by +5.3% and curated ImageNet by +0.4%. 1 * Work done during an internship at Meta AI. Correspondence to: swmo@kaist.ac.kr 1 Code: | Published as a conference paper at ICLR 2023 ROPAWS: ROBUST SEMI-SUPERVISED REPRESENTA- TION LEARNING FROM UNCURATED DATA |
d211117443 | Graph neural networks (GNNs) are a class of deep models that operate on data with arbitrary topology represented as graphs. We introduce an efficient memory layer for GNNs that can jointly learn node representations and coarsen the graph. We also introduce two new networks based on this layer: memory-based GNN (MemGNN) and graph memory network (GMN) that can learn hierarchical graph representations. The experimental results show that the proposed models achieve state-of-the-art results in eight out of nine graph classification and regression benchmarks. We also show that the learned representations could correspond to chemical features in the molecule data. Code and reference implementations are released at: https://github.com/amirkhas/GraphMemoryNet * Work done during internship at Autodesk Toronto AI Lab. | Published as a conference paper at ICLR 2020 MEMORY-BASED GRAPH NETWORKS |
d232135120 | With sequentially stacked self-attention, (optional) encoder-decoder attention, and feed-forward layers, Transformer achieves big success in natural language processing (NLP), and many variants have been proposed. Currently, almost all these models assume that the layer order is fixed and kept the same across data samples. We observe that different data samples actually favor different orders of the layers. Based on this observation, in this work, we break the assumption of the fixed layer order in Transformer and introduce instance-wise layer reordering into model structure. Our Instance-wise Ordered Transformer (IOT) can model variant functions by reordered layers, which enables each sample to select the better one to improve the model performance under the constraint of almost same number of parameters. To achieve this, we introduce a light predictor with negligible parameter and inference cost to decide the most capable and favorable layer order for any input sequence. Experiments on 3 tasks (neural machine translation, abstractive summarization, and code generation) and 9 datasets demonstrate consistent improvements of our method. We further show that our method can also be applied to other architectures beyond Transformer. Our code is released at Github 1 . * Equal contribution and corresponding authors.Referenceand just like that , the iceberg shows you a different side of its personality . BLEU↑ TER↓ | Published as a conference paper at ICLR 2021 IOT: INSTANCE-WISE LAYER REORDERING FOR TRANSFORMER STRUCTURES |
d258865243 | Understanding the interaction between multiple agents is crucial for realistic vehicle trajectory prediction. Existing methods have attempted to infer the interaction from the observed past trajectories of agents using pooling, attention, or graph-based methods, which rely on a deterministic approach. However, these methods can fail under complex road structures, as they cannot predict various interactions that may occur in the future. In this paper, we propose a novel approach that uses lane information to predict a stochastic future relationship among agents. To obtain a coarse future motion of agents, our method first predicts the probability of lanelevel waypoint occupancy of vehicles. We then utilize the temporal probability of passing adjacent lanes for each agent pair, assuming that agents passing adjacent lanes will highly interact. We also model the interaction using a probabilistic distribution, which allows for multiple possible future interactions. The distribution is learned from the posterior distribution of interaction obtained from ground truth future trajectories. We validate our method on popular trajectory prediction datasets: nuScenes and Argoverse. The results show that the proposed method brings remarkable performance gain in prediction accuracy, and achieves state-ofthe-art performance in long-term prediction benchmark dataset. | Published as a conference paper at ICLR 2023 LEVERAGING FUTURE RELATIONSHIP REASONING FOR VEHICLE TRAJECTORY PREDICTION |
d12122362 | Despite recent advances, memory-augmented deep neural networks are still limited when it comes to life-long and one-shot learning, especially in remembering rare events. We present a large-scale life-long memory module for use in deep learning. The module exploits fast nearest-neighbor algorithms for efficiency and thus scales to large memory sizes. Except for the nearest-neighbor query, the module is fully differentiable and trained end-to-end with no extra supervision. It operates in a life-long manner, i.e., without the need to reset it during training. Our memory module can be easily added to any part of a supervised neural network. To show its versatility we add it to a number of networks, from simple convolutional ones tested on image classification to deep sequence-to-sequence and recurrent-convolutional models. In all cases, the enhanced network gains the ability to remember and do life-long one-shot learning. Our module remembers training examples shown many thousands of steps in the past and it can successfully generalize from them. We set new state-of-the-art for one-shot learning on the Omniglot dataset and demonstrate, for the first time, life-long one-shot learning in recurrent neural networks on a large-scale machine translation task. * First two authors contributed equally. † Work done as a member of the Google Brain Residency program (g.co/brainresidency). ‡ Work done during internship at Google Brain. | Published as a conference paper at ICLR 2017 LEARNING TO REMEMBER RARE EVENTS |
d399358 | Representation learning is the dominant technique for unsupervised domain adaptation, but existing approaches have two major weaknesses. First, they often require the specification of "pivot features" that generalize across domains, which are selected by taskspecific heuristics. We show that a novel but simple feature embedding approach provides better performance, by exploiting the feature template structure common in NLP problems. | Unsupervised Multi-Domain Adaptation with Feature Embeddings |
d256627465 | Rapidly learning from ongoing experiences and remembering past events with a flexible memory system are two core capacities of biological intelligence. While the underlying neural mechanisms are not fully understood, various evidence supports that synaptic plasticity plays a critical role in memory formation and fast learning. Inspired by these results, we equip Recurrent Neural Networks (RNNs) with plasticity rules to enable them to adapt their parameters according to ongoing experiences. In addition to the traditional local Hebbian plasticity, we propose a global, gradient-based plasticity rule, which allows the model to evolve towards its self-determined target. Our models show promising results on sequential and associative memory tasks, illustrating their ability to robustly form and retain memories. In the meantime, these models can cope with many challenging few-shot learning problems. Comparing different plasticity rules under the same framework shows that Hebbian plasticity is well-suited for several memory and associative learning tasks; however, it is outperformed by gradient-based plasticity on fewshot regression tasks which require the model to infer the underlying mapping. | Published as a conference paper at ICLR 2023 HEBBIAN AND GRADIENT-BASED PLASTICITY EN- ABLES ROBUST MEMORY AND RAPID LEARNING IN RNNS |
d257405409 | In many retrieval systems the original high dimensional data (e.g., images) is mapped to a lower dimensional feature through a learned embedding model. The task of retrieving the most similar data from a gallery set to a given query data is performed through a similarity comparison on features. When the embedding model is updated, it might produce features that are not comparable/compatible with features already in the gallery computed with the old model. Subsequently, all features in the gallery need to be re-computed using the new embedding model -a computationally expensive process called backfilling. Recently, compatible representation learning methods have been proposed to avoid backfilling. Despite their relative success, there is an inherent trade-off between the new model performance and its compatibility with the old model. In this work, we introduce FastFill: a compatible model update process using feature alignment and policy based partial backfilling to promptly elevate retrieval performance. We show that previous backfilling strategies suffer from decreased performance and demonstrate the importance of both the training objective and the ordering in online partial backfilling. We propose a new training method for feature alignment between old and new embedding models using uncertainty estimation. Compared to previous works, we obtain significantly improved backfilling results on a variety of datasets: mAP on ImageNet (+4.4%), Places-365 (+2.7%), and VGG-Face2 (+1.3%). Further, we demonstrate that when updating a biased model with FastFill, the minority subgroup accuracy gap promptly vanishes with a small fraction of partial backfilling. 1 | Published as a conference paper at ICLR 2023 FASTFILL: EFFICIENT COMPATIBLE MODEL UPDATE |
d220935959 | We explore value-based multi-agent reinforcement learning (MARL) in the popular paradigm of centralized training with decentralized execution (CTDE). CTDE has an important concept, Individual-Global-Max (IGM) principle, which requires the consistency between joint and local action selections to support efficient local decision-making. However, in order to achieve scalability, existing MARL methods either limit representation expressiveness of their value function classes or relax the IGM consistency, which may suffer from instability risk or may not perform well in complex domains. This paper presents a novel MARL approach, called duPLEX dueling multi-agent Q-learning (QPLEX), which takes a duplex dueling network architecture to factorize the joint value function. This duplex dueling structure encodes the IGM principle into the neural network architecture and thus enables efficient value function learning. Theoretical analysis shows that QPLEX achieves a complete IGM function class. Empirical experiments on StarCraft II micromanagement tasks demonstrate that QPLEX significantly outperforms stateof-the-art baselines in both online and offline data collection settings, and also reveal that QPLEX achieves high sample efficiency and can benefit from offline datasets without additional online exploration 1 . * Equal contribution.Published as a conference paper at ICLR 2021 ∞ t=0 γ t r t |s 0 = s, π]. Another quantity of interest in policy search is the joint action-value function Q π (s, a) = r(s, a) + γE s [V π (s )].DEEP MULTI-AGENT Q-LEARNING IN DEC-POMDPQ-learning algorithms is a popular algorithm to find the optimal joint action-value function Q * (s, a) = r(s, a)+γE s [max a Q * (s , a )]. Deep Q-learning represents the action-value function | Published as a conference paper at ICLR 2021 QPLEX: DUPLEX DUELING MULTI-AGENT Q-LEARNING |
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