pwc-archive/papers-with-abstracts huggingface dataset:
When Is Generalizable Reinforcement Learning Tractable?
Authors: Dhruv Malik, Yuanzhi Li, Pradeep Ravikumar
Date: 2021-01-01 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

Agents trained by reinforcement learning (RL) often fail to generalize beyond the environment they were trained in, even when presented with new scenarios that seem similar to the training environment. We study the query complexity required to train RL agents that generalize to multiple environments. Intuitively, tractable generalization is only possible when the environments are similar or close in some sense. To capture this, we introduce Weak Proximity, a natural structural condition that requires the environments to have highly similar transition and reward functions and share a policy providing optimal value. Despite such shared structure, we prove that tractable generalization is impossible in the worst case. This holds even when each individual environment can be efficiently solved to obtain an optimal linear policy, and when the agent possesses a generative model. Our lower bound applies to the more complex task of representation learning for the purpose of efficient generalization to multiple environments. On the positive side, we introduce Strong Proximity, a strengthened condition which we prove is sufficient for efficient generalization.
Analyzing Commonsense Emergence in Few-shot Knowledge Models
Authors: Jeff Da, Ronan Le Bras, Ximing Lu, Yejin Choi, Antoine Bosselut
Date: 2021-01-01 00:00:00
Proceeding: AKBC 2021 10

Recently, commonsense knowledge models - pretrained language models (LM) fine-tuned on knowledge graph (KG) tuples - showed that considerable amounts of commonsense knowledge can be encoded in the parameters of large language models. However, as parallel studies show that LMs are poor hypothesizers of declarative commonsense relationships on their own, it remains unclear whether this knowledge is learned during pretraining or from fine-tuning on KG examples. To investigate this question, we train commonsense knowledge models in few-shot settings to study the emergence of their commonsense representation abilities. Our results show that commonsense knowledge models can rapidly adapt from limited examples, indicating that KG fine-tuning serves to learn an interface to encoded knowledge learned during pretraining. Importantly, our analysis of absolute, angular, and distributional parameter changes during few-shot fine-tuning provides novel insights into how this interface is learned.
Iranis: A Large-scale Dataset of Farsi License Plate Characters
Authors: Ali Tourani, Sajjad Soroori, Asadollah Shahbahrami, Alireza Akoushideh
Date: 2021-01-01 00:00:00
Tasks: image-classification, Image Classification, License Plate Detection, object-detection, Object Detection, Optical Character Recognition, Optical Character Recognition (OCR)

Providing huge amounts of data is a fundamental demand when dealing with Deep Neural Networks (DNNs). Employing these algorithms to solve computer vision problems resulted in the advent of various image datasets to feed the most common visual imagery deep structures, known as Convolutional Neural Networks (CNNs). In this regard, some datasets can be found that contain hundreds or even thousands of images for license plate detection and optical character recognition purposes. However, no publicly available image dataset provides such data for the recognition of Farsi characters used in car license plates. The gap has to be filled due to the numerous advantages of developing accurate deep learning-based systems for law enforcement and surveillance purposes. This paper introduces a large-scale dataset that includes images of numbers and characters used in Iranian car license plates. The dataset, named Iranis, contains more than 83,000 images of Farsi numbers and letters collected from real-world license plate images captured by various cameras. The variety of instances in terms of camera shooting angle, illumination, resolution, and contrast make the dataset a proper choice for training DNNs. Dataset images are manually annotated for object detection and image classification. Finally, and to build a baseline for Farsi character recognition, the paper provides a performance analysis using a YOLO v.3 object detector.
Rider: Reader-Guided Passage Reranking for Open-Domain Question Answering
Authors: Yuning Mao, Pengcheng He, Xiaodong Liu, Yelong Shen, Jianfeng Gao, Jiawei Han, Weizhu Chen
Date: 2021-01-01 00:00:00
Tasks: Natural Questions, Open-Domain Question Answering, Passage Reranking, Question Answering, Reranking, Retrieval, TriviaQA

Current open-domain question answering systems often follow a Retriever-Reader architecture, where the retriever first retrieves relevant passages and the reader then reads the retrieved passages to form an answer. In this paper, we propose a simple and effective passage reranking method, named Reader-guIDEd Reranker (RIDER), which does not involve training and reranks the retrieved passages solely based on the top predictions of the reader before reranking. We show that RIDER, despite its simplicity, achieves 10 to 20 absolute gains in top-1 retrieval accuracy and 1 to 4 Exact Match (EM) gains without refining the retriever or reader. In addition, RIDER, without any training, outperforms state-of-the-art transformer-based supervised rerankers. Remarkably, RIDER achieves 48.3 EM on the Natural Questions dataset and 66.4 EM on the TriviaQA dataset when only 1,024 tokens (7.8 passages on average) are used as the reader input after passage reranking.
Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models
Authors: Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, Daniel S. Weld
Date: 2021-01-01 00:00:00
Proceeding: ACL 2021 5
Tasks: counterfactual, Text Generation

While counterfactual examples are useful for analysis and training of NLP models, current generation methods either rely on manual labor to create very few counterfactuals, or only instantiate limited types of perturbations such as paraphrases or word substitutions. We present Polyjuice, a general-purpose counterfactual generator that allows for control over perturbation types and locations, trained by finetuning GPT-2 on multiple datasets of paired sentences. We show that Polyjuice produces diverse sets of realistic counterfactuals, which in turn are useful in various distinct applications: improving training and evaluation on three different tasks (with around 70% less annotation effort than manual generation), augmenting state-of-the-art explanation techniques, and supporting systematic counterfactual error analysis by revealing behaviors easily missed by human experts.
An Ontology Design Pattern for representing Recurrent Situations
Authors: Valentina Anita Carriero, Aldo Gangemi, Andrea Giovanni Nuzzolese, Valentina Presutti
Date: 2021-01-01 00:00:00

In this paper, we present an Ontology Design Pattern for representing situations that recur at regular periods and share some invariant factors, which unify them conceptually: we refer to this set of recurring situations as recurrent situation series. The proposed pattern appears to be foundational, since it can be generalised for modelling the top-level domain-independent concept of recurrence, which is strictly associated with invariance. The pattern reuses other foundational patterns such as Collection, Description and Situation, Classification, Sequence. Indeed, a recurrent situation series is formalised as both a collection of situations occurring regularly over time and unified according to some properties that are common to all the members, and a situation itself, which provides a relational context to its members that satisfy a reference description. Besides including some exemplifying instances of this pattern, we show how it has been implemented and specialised to model recurrent cultural events and ceremonies in ArCo, the Knowledge Graph of Italian cultural heritage.
A General Counterexample to Any Decision Theory and Some Responses
Authors: Joar Skalse
Date: 2021-01-01 00:00:00
Tasks: Position

In this paper I present an argument and a general schema which can be used to construct a problem case for any decision theory, in a way that could be taken to show that one cannot formulate a decision theory that is never outperformed by any other decision theory. I also present and discuss a number of possible responses to this argument. One of these responses raises the question of what it means for two decision problems to be "equivalent" in the relevant sense, and gives an answer to this question which would invalidate the first argument. However, this position would have further consequences for how we compare different decision theories in decision problems already discussed in the literature (including e.g. Newcomb's problem).
VisualSparta: An Embarrassingly Simple Approach to Large-scale Text-to-Image Search with Weighted Bag-of-words
Authors: Xiaopeng Lu, Tiancheng Zhao, Kyusong Lee
Date: 2021-01-01 00:00:00
Proceeding: ACL 2021 5
Tasks: CPU, Cross-Modal Information Retrieval, Cross-Modal Retrieval, GPU, Image Retrieval, Information Retrieval, Retrieval

Text-to-image retrieval is an essential task in cross-modal information retrieval, i.e., retrieving relevant images from a large and unlabelled dataset given textual queries. In this paper, we propose VisualSparta, a novel (Visual-text Sparse Transformer Matching) model that shows significant improvement in terms of both accuracy and efficiency. VisualSparta is capable of outperforming previous state-of-the-art scalable methods in MSCOCO and Flickr30K. We also show that it achieves substantial retrieving speed advantages, i.e., for a 1 million image index, VisualSparta using CPU gets ~391X speedup compared to CPU vector search and ~5.4X speedup compared to vector search with GPU acceleration. Experiments show that this speed advantage even gets bigger for larger datasets because VisualSparta can be efficiently implemented as an inverted index. To the best of our knowledge, VisualSparta is the first transformer-based text-to-image retrieval model that can achieve real-time searching for large-scale datasets, with significant accuracy improvement compared to previous state-of-the-art methods.
Code Generation from Natural Language with Less Prior and More Monolingual Data
Authors: Sajad Norouzi, Keyi Tang, Yanshuai Cao
Date: 2021-01-01 00:00:00
Tasks: Code Generation, Inductive Bias, Semantic Parsing

Training datasets for semantic parsing are typically small due to the higher expertise required for annotation than most other NLP tasks. As a result, models for this application usually need additional prior knowledge to be built into the architecture or algorithm. The increased dependency on human experts hinders automation and raises the development and maintenance costs in practice. This work investigates whether a generic transformer-based seq2seq model can achieve competitive performance with minimal code-generation-specific inductive bias design. By exploiting a relatively sizeable monolingual corpus of the target programming language, which is cheap to mine from the web, we achieved 81.03% exact match accuracy on Django and 32.57 BLEU score on CoNaLa. Both are SOTA to the best of our knowledge. This positive evidence highlights a potentially easier path toward building accurate semantic parsers in practice.
Efficient Learning-based Scheduling for Information Freshness in Wireless Networks
Authors: Bin Li
Date: 2021-01-01 00:00:00
Tasks: Decision Making, Scheduling

Motivated by the recent trend of integrating artificial intelligence into the Internet-of-Things (IoT), we consider the problem of scheduling packets from multiple sensing sources to a central controller over a wireless network. Here, packets from different sensing sources have different values or degrees of importance to the central controller for intelligent decision making. In such a setup, it is critical to provide timely and valuable information for the central controller. In this paper, we develop a parameterized maximum-weight type scheduling policy that combines both the AoI metrics and Upper Confidence Bound (UCB) estimates in its weight measure with parameter $\eta$. Here, UCB estimates balance the tradeoff between exploration and exploitation in learning and are critical for yielding a small cumulative regret. We show that our proposed algorithm yields the running average total age at most by $O(N^2\eta)$. We also prove that our proposed algorithm achieves the cumulative regret over time horizon $T$ at most by $O(NT/\eta+\sqrt{NT\log T})$. This reveals a tradeoff between the cumulative regret and the running average total age: when increasing $\eta$, the cumulative regret becomes smaller, but is at the cost of increasing running average total age. Simulation results are provided to evaluate the efficiency of our proposed algorithm.
The Bayesian Method of Tensor Networks
Authors: Erdong Guo, David Draper
Date: 2021-01-01 00:00:00
Tasks: Tensor Networks

Bayesian learning is a powerful learning framework which combines the external information of the data (background information) with the internal information (training data) in a logically consistent way in inference and prediction. By Bayes rule, the external information (prior distribution) and the internal information (training data likelihood) are combined coherently, and the posterior distribution and the posterior predictive (marginal) distribution obtained by Bayes rule summarize the total information needed in the inference and prediction, respectively. In this paper, we study the Bayesian framework of the Tensor Network from two perspective. First, we introduce the prior distribution to the weights in the Tensor Network and predict the labels of the new observations by the posterior predictive (marginal) distribution. Since the intractability of the parameter integral in the normalization constant computation, we approximate the posterior predictive distribution by Laplace approximation and obtain the out-product approximation of the hessian matrix of the posterior distribution of the Tensor Network model. Second, to estimate the parameters of the stationary mode, we propose a stable initialization trick to accelerate the inference process by which the Tensor Network can converge to the stationary path more efficiently and stably with gradient descent method. We verify our work on the MNIST, Phishing Website and Breast Cancer data set. We study the Bayesian properties of the Bayesian Tensor Network by visualizing the parameters of the model and the decision boundaries in the two dimensional synthetic data set. For a application purpose, our work can reduce the overfitting and improve the performance of normal Tensor Network model.
A Survey on Deep Reinforcement Learning for Audio-Based Applications
Authors: Siddique Latif, Heriberto Cuayáhuitl, Farrukh Pervez, Fahad Shamshad, Hafiz Shehbaz Ali, Erik Cambria
Date: 2021-01-01 00:00:00
Tasks: Audio Signal Processing, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Survey

Deep reinforcement learning (DRL) is poised to revolutionise the field of artificial intelligence (AI) by endowing autonomous systems with high levels of understanding of the real world. Currently, deep learning (DL) is enabling DRL to effectively solve various intractable problems in various fields. Most importantly, DRL algorithms are also being employed in audio signal processing to learn directly from speech, music and other sound signals in order to create audio-based autonomous systems that have many promising application in the real world. In this article, we conduct a comprehensive survey on the progress of DRL in the audio domain by bringing together the research studies across different speech and music-related areas. We begin with an introduction to the general field of DL and reinforcement learning (RL), then progress to the main DRL methods and their applications in the audio domain. We conclude by presenting challenges faced by audio-based DRL agents and highlighting open areas for future research and investigation.
Adam revisited: a weighted past gradients perspective
Authors: Hui Zhong, Zaiyi Chen, Chuan Qin, Zai Huang, Vincent W. Zheng, Tong Xu, Enhong Chen
Date: 2021-01-01 00:00:00

Adaptive learning rate methods have been successfully applied in many fields, especially in training deep neural networks. Recent results have shown that adaptive methods with exponential increasing weights on squared past gradients (i.e., ADAM, RMSPROP) may fail to converge to the optimal solution. Though many algorithms, such as AMSGRAD and ADAMNC, have been proposed to fix the non-convergence issues, achieving a data-dependent regret bound similar to or better than ADAGRAD is still a challenge to these methods. In this paper, we propose a novel adaptive method weighted adaptive algorithm (WADA) to tackle the non-convergence issues. Unlike AMSGRAD and ADAMNC, we consider using a milder growing weighting strategy on squared past gradient, in which weights grow linearly. Based on this idea, we propose weighted adaptive gradient method framework (WAGMF) and implement WADA algorithm on this framework. Moreover, we prove that WADA can achieve a weighted data-dependent regret bound, which could be better than the original regret bound of ADAGRAD when the gradients decrease rapidly. This bound may partially explain the good performance of ADAM in practice. Finally, extensive experiments demonstrate the effectiveness of WADA and its variants in comparison with several variants of ADAM on training convex problems and deep neural networks.
On Stochastic Variance Reduced Gradient Method for Semidefinite Optimization
Authors: Jinshan Zeng, Yixuan Zha, Ke Ma, Yuan YAO
Date: 2021-01-01 00:00:00
Tasks: Computational Efficiency

The low-rank stochastic semidefinite optimization has attracted rising attention due to its wide range of applications. The nonconvex reformulation based on the low-rank factorization, significantly improves the computational efficiency but brings some new challenge to the analysis. The stochastic variance reduced gradient (SVRG) method has been regarded as one of the most effective methods. SVRG in general consists of two loops, where a reference full gradient is first evaluated in the outer loop and then used to yield a variance reduced estimate of the current gradient in the inner loop. Two options have been suggested to yield the output of the inner loop, where Option I sets the output as its last iterate, and Option II yields the output via random sampling from all the iterates in the inner loop. However, there is a significant gap between the theory and practice of SVRG when adapted to the stochastic semidefinite programming (SDP). SVRG practically works better with Option I, while most of existing theoretical results focus on Option II. In this paper, we fill this gap via exploiting a new semi-stochastic variant of the original SVRG with Option I adapted to the semidefinite optimization. Equipped with this, we establish the global linear submanifold convergence (i.e., converging exponentially fast to a submanifold of a global minimum under the orthogonal group action) of the proposed SVRG method, given a provable initialization scheme and under certain smoothness and restricted strongly convex assumptions. Our analysis includes the effects of the mini-batch size and update frequency in the inner loop as well as two practical step size strategies, the fixed and stabilized Barzilai-Borwein step sizes. Some numerical results in matrix sensing demonstrate the efficiency of proposed SVRG method outperforming Option II counterpart as well as others.
Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers
Authors: Machel Reid, Edison Marrese-Taylor, Yutaka Matsuo
Date: 2021-01-01 00:00:00
Proceeding: Findings (EMNLP) 2021 11
Tasks: Abstractive Text Summarization, Language Modeling, Language Modelling, Machine Translation, Translation

Transformers have shown improved performance when compared to previous architectures for sequence processing such as RNNs. Despite their sizeable performance gains, as recently suggested, the model is computationally expensive to train and with a high parameter budget. In light of this, we explore parameter-sharing methods in Transformers with a specific focus on generative models. We perform an analysis of different parameter sharing/reduction methods and develop the Subformer. Our model combines sandwich-style parameter sharing, which overcomes naive cross-layer parameter sharing in generative models, and self-attentive embedding factorization (SAFE). Experiments on machine translation, abstractive summarization and language modeling show that the Subformer can outperform the Transformer even when using significantly fewer parameters.
Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?
Authors: Jun Ma
Date: 2021-01-01 00:00:00
Tasks: All, Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Prognosis, Segmentation, Semantic Segmentation

Segmentation is one of the most important and popular tasks in medical image analysis, which plays a critical role in disease diagnosis, surgical planning, and prognosis evaluation. During the past five years, on the one hand, thousands of medical image segmentation methods have been proposed for various organs and lesions in different medical images, which become more and more challenging to fairly compare different methods. On the other hand, international segmentation challenges can provide a transparent platform to fairly evaluate and compare different methods. In this paper, we present a comprehensive review of the top methods in ten 3D medical image segmentation challenges during 2020, covering a variety of tasks and datasets. We also identify the "happy-families" practices in the cutting-edge segmentation methods, which are useful for developing powerful segmentation approaches. Finally, we discuss open research problems that should be addressed in the future. We also maintain a list of cutting-edge segmentation methods at \url{https://github.com/JunMa11/SOTA-MedSeg}.
Adaptive Deconvolution-based stereo matching Net for Local Stereo Matching
Authors: Xin Ma, Zhicheng Zhang, Danfeng Wang, Yu Luo, Hui Yuan
Date: 2021-01-01 00:00:00
Tasks: Stereo Matching

In deep learning-based local stereo matching methods, larger image patches usually bring better stereo matching accuracy. However, it is unrealistic to increase the size of the image patch size without restriction. Arbitrarily extending the patch size will change the local stereo matching method into the global stereo matching method, and the matching accuracy will be saturated. We simplified the existing Siamese convolutional network by reducing the number of network parameters and propose an efficient CNN based structure, namely Adaptive Deconvolution-based disparity matching Net (ADSM net) by adding deconvolution layers to learn how to enlarge the size of input feature map for the following convolution layers. Experimental results on the KITTI 2012 and 2015 datasets demonstrate that the proposed method can achieve a good trade-off between accuracy and complexity.