pwc-archive/papers-with-abstracts huggingface dataset:
An Attention-LSTM Hybrid Model for the Coordinated Routing of Multiple Vehicles
Authors: Aigerim Bogyrbayeva, Taehyun Yoon, Hanbum Ko, Sungbin Lim, Hyokun Yun, Changhyun Kwon
Date: 2021-09-29 00:00:00
Tasks: Combinatorial Optimization, Computational Efficiency, Decoder, Traveling Salesman Problem

Reinforcement learning has recently shown promise in learning quality solutions in a number of combinatorial optimization problems. In particular, the attention-based encoder-decoder models show high effectiveness on various routing problems, including the Traveling Salesman Problem (TSP).  Unfortunately, they perform poorly for the TSP with Drones (TSP-D), requiring routing a heterogeneous fleet of vehicles in coordination. In TSP-D, two different types of vehicles are moving in tandem and may need to wait at a node for the other vehicle to join. State-less attention-based decoder fails to make such coordination between vehicles. We propose an attention encoder-LSTM decoder hybrid model, in which the decoder's hidden state can represent the sequence of actions made. We empirically demonstrate that such a hybrid model improves upon a purely attention-based model for both solution quality and computational efficiency. Our experiments on the min-max Capacitated Vehicle Routing Problem (mmCVRP) also confirm that the hybrid model is more suitable for coordinated routing of multiple vehicles than the attention-based model.
Two Birds, One Stone: Achieving both Differential Privacy and Certified Robustness for Pre-trained Classifiers via Input Perturbation
Authors: Pengfei Tang, Wenjie Wang, Xiaolan Gu, Jian Lou, Li Xiong, Ming Li
Date: 2021-09-29 00:00:00
Tasks: image-classification, Image Classification

Recent studies have shown that pre-trained classifiers are increasingly powerful to improve the performance on different tasks, e.g, neural language processing, image classification. However, adversarial examples from attackers can trick pre-trained classifiers to misclassify. To solve this challenge, a reconstruction network is built before the public pre-trained classifiers to offer certified robustness and defend against adversarial examples through input perturbation. On the other hand, the reconstruction network requires training on the dataset, which incurs privacy leakage of training data through inference attacks. To prevent this leakage, differential privacy (DP) is applied to offer a provable privacy guarantee on training data through gradient perturbation. Most existing works employ certified robustness and DP independently and fail to exploit the fact that input perturbation designed to achieve certified robustness can achieve (partial) DP. In this paper, we propose perturbation transformation to show how the input perturbation designed for certified robustness can be transformed into gradient perturbation during training. We propose Multivariate Gaussian mechanism to analyze the privacy guarantee of this transformed gradient perturbation and precisely quantify the level of DP achieved by input perturbation. To satisfy the overall DP requirement, we add additional gradient perturbation during training and propose Mixed Multivariate Gaussian Analysis to analyze the privacy guarantee provided by the transformed gradient perturbation and additional gradient perturbation. Moreover, we prove that Mixed Multivariate Gaussian Analysis can work with moments accountant to provide a tight DP estimation. Extensive experiments on benchmark datasets show that our framework significantly outperforms state-of-the-art methods and achieves better accuracy and robustness under the same privacy guarantee.
EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning
Authors: Shuang Ao, Tianyi Zhou, Jing Jiang, Guodong Long, Xuan Song, Chengqi Zhang
Date: 2021-09-29 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Reinforcement learning (RL)'s efficiency can drastically degrade on long-horizon tasks due to sparse rewards and the RL policy can be fragile to small changes in deployed environments. To improve RL's efficiency and generalization to varying environments, we study how to automatically generate a curriculum of tasks with coupled environments for RL. To this end, we train two curriculum policies together with RL: (1) a co-operative planning policy recursively decomposing a hard task into coarse-to-fine sub-task sequences as a tree; and (2) an adversarial policy modifying the environment (e.g., position/size of obstacles) in each sub-task. They are complementary in acquiring more informative feedback for RL: the planning policy provides dense reward of finishing easier sub-tasks while the environment policy modifies these sub-tasks to be adequately challenging and diverse so the RL agent can quickly adapt to different tasks/environments. On the other hand, they are trained using the RL agent's dense feedback on sub-tasks so the sub-task curriculum keeps adaptive to the agent's progress via this ``iterative mutual-boosting'' scheme. Moreover, the sub-task tree naturally enables an easy-to-hard curriculum for every policy: its top-down construction gradually increases sub-tasks the planning policy needs to generate, while the adversarial training between the environment policy and the RL policy follows a bottom-up traversal that starts from a dense sequence of easier sub-tasks allowing more frequent modifications to the environment. Therefore, jointly training the three policies leads to efficient RL guided by a curriculum progressively improving the sparse reward and generalization. We compare our method with popular RL/planning approaches targeting similar problems and the ones with environment generators or adversarial agents. Thorough experiments on diverse benchmark tasks demonstrate significant advantages of our method on improving RL's efficiency and generalization.
Sneakoscope: Revisiting Unsupervised Out-of-Distribution Detection
Authors: Tianji Cong, Atul Prakash
Date: 2021-09-29 00:00:00
Tasks: Out-of-Distribution Detection, Out of Distribution (OOD) Detection

The problem of detecting out-of-distribution (OOD) examples in neural networks has been widely studied in the literature, with state-of-the-art techniques being supervised in that they require fine-tuning on OOD data to achieve high-quality OOD detection. But supervised OOD detection methods also have a disadvantage in that they require expensive training on OOD data, curating the OOD dataset so that it is distinguishable from the in-distribution data, and significant hyper-parameter tuning. In this work, we propose a unified evaluation suite, Sneakoscope, to revisit the problem with in-depth exploration of unsupervised OOD detection. Our surprising discovery shows that (1) model architectures play a significant role in unsupervised OOD detection performance; (2) unsupervised approaches applied on large-scale pre-trained models can achieve competitive performance compared to their supervised counterparts; and (3) unsupervised OOD detection based on Mahalanobis Distance with the support of a pre-trained model consistently outperforms other unsupervised methods by a large margin and compares favorably with results from state-of-the-art supervised OOD detection methods reported in the literature. We thus provide new baselines for unsupervised OOD detection methods.
WHICH SAMPLES SHOULD BE LEARNED FIRST：EASY OR HARD？
Authors: Xiaoling Zhou, Ou wu
Date: 2021-09-29 00:00:00

An effective weighting scheme for training samples is essential for learning tasks. Numerous weighting schemes have been proposed. Some schemes take the easy-first mode on samples, whereas some others take the hard-first mode. Naturally, an interesting yet realistic question is raised. Which samples should be learned first given a new learning task, easy or hard? To answer this question, three aspects of research are carried out. First, a high-level unified weighted loss is proposed, providing a more comprehensive view for existing schemes. Theoretical analysis is subsequently conducted and preliminary conclusions are obtained. Second, a flexible weighting scheme is proposed to overcome the defects of existing schemes. The three modes, namely, easy/medium/hard-first, can be flexibly switched in the proposed scheme. Third, a wide range of experiments are conducted to further compare the weighting schemes in different modes. On the basis of these works, reasonable answers are obtained. Factors including prior knowledge and data characteristics determine which samples should be learned first in a learning task.
Speech-MLP: a simple MLP architecture for speech processing
Authors: Chao Xing, Dong Wang, LiRong Dai, Qun Liu, Anderson Avila
Date: 2021-09-29 00:00:00
Tasks: Keyword Spotting, Speech Enhancement, speech-recognition, Speech Recognition, Speech Synthesis

Overparameterized transformer-based architectures have shown remarkable performance in recent years, achieving state-of-the-art results in speech processing tasks such as speech recognition, speech synthesis, keyword spotting, and speech enhancement et al. The main assumption is that with the underlying self-attention mechanism, transformers can ultimately capture the long-range temporal dependency from speech signals. In this paper, we propose a multi-layer perceptron (MLP) architecture, namely speech-MLP, useful for extracting information from speech signals. The model splits feature channels into non-overlapped chunks and processes each chunk individually. The processed chunks are then merged together and processed to consolidate the output. By setting the different numbers of chunks and focusing on different contextual window sizes, speech-MLP learns multiscale local temporal dependency. The proposed model is successfully evaluated on two tasks: keyword spotting and speech enhancement. In our experiments, we use two benchmark datasets for keyword spotting (Google speech command V2-35 and LibriWords) and the VoiceBank dataset for the speech enhancement task. In all experiments, speech-MLP surpassed transformer-based solutions, achieving state-of-the-art performance with fewer parameters and simpler training schemes. Such results indicate that oftentimes more complex models such as transformers are not necessary for speech processing tasks. Hence, they should not be considered as the first option as simpler and more compact models can offer optimal performance.
Automatic Loss Function Search for Predict-Then-Optimize Problems with Strong Ranking Property
Authors: Boshi Wang, Jialin Yi, Hang Dong, Bo Qiao, Chuan Luo, QIngwei Lin
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Combinatorial Optimization, Decision Making, Prediction

Combinatorial optimization problems with parameters to be predicted from side information are commonly seen in a variety of problems during the paradigm shift from reactive decision making to proactive decision making. Due to the misalignment between the continuous prediction results and the discrete decisions in optimization problems, it is hard to achieve a satisfactory prediction result with the ordinary $l_2$ loss in the prediction phase. To properly connect the prediction loss with the optimization goal, in this paper we propose a total group preorder (TGP) loss and its differential version called approximated total group preorder (ATGP) loss for predict-then-optimize (PTO) problems with strong ranking property. These new losses are provably more robust than the usual $l_2$ loss in a linear regression setting and have great potential to extend to other settings. We also propose an automatic searching algorithm that adapts the ATGP loss to PTO problems with different combinatorial structures. Extensive experiments on the ranking problem, the knapsack problem, and the shortest path problem have demonstrated that our proposed method can achieve a significant performance compared to the other methods designed for PTO problems.
Learning Sample Reweighting for Adversarial Robustness
Authors: Chester Holtz, Tsui-Wei Weng, Gal Mishne
Date: 2021-09-29 00:00:00
Tasks: Adversarial Robustness, Bilevel Optimization

There has been great interest in enhancing the robustness of neural network classifiers to defend against adversarial perturbations through adversarial training, while balancing the trade-off between robust accuracy and standard accuracy. We propose a novel adversarial training framework that learns to reweight the loss associated with individual training samples based on a notion of class-conditioned margin, with the goal of improving robust generalization. Inspired by MAML-based approaches, we formulate weighted adversarial training as a bilevel optimization problem where the upper-level task corresponds to learning a robust classifier, and the lower-level task corresponds to learning a parametric function that maps from a sample's \textit{multi-class margin} to an importance weight. Extensive experiments demonstrate that our approach improves both clean and robust accuracy compared to related techniques and state-of-the-art baselines.
Ensembles and Cocktails: Robust Finetuning for Natural Language Generation
Authors: John Hewitt, Xiang Lisa Li, Sang Michael Xie, Benjamin Newman, Percy Liang
Date: 2021-09-29 00:00:00
Tasks: Language Modelling, Text Generation

When finetuning a pretrained language model for natural language generation tasks, one is currently faced with a tradeoff. Lightweight finetuning (e.g., prefix-tuning, adapters), which freezes all or most of the parameters of the pretrained model, has been shown to achieve stronger out-of-distribution (OOD) performance than full finetuning, which tunes all of the parameters. However, lightweight finetuning can underperform full finetuning in-distribution (ID). In this work, we present methods to combine the benefits of full and lightweight finetuning, achieving strong performance both ID and OOD. First, we show that an ensemble of the lightweight and full finetuning models achieves the best of both worlds: performance matching the better of full and lightweight finetuning, both ID and OOD. Second, we show that we can achieve similar improvements using a single model instead of two with our proposed cocktail finetuning, which augments full finetuning via distillation from a lightweight model. Finally, we provide some explanatory theory in a multiclass logistic regression setting with a large number of classes, describing how distillation on ID data can transfer the OOD behavior of one model to another.
Toward Faithful Case-based Reasoning through Learning Prototypes in a Nearest Neighbor-friendly Space.
Authors: Seyed Omid Davoudi, Majid Komeili
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: BIG-bench Machine Learning

Recent advances in machine learning have brought opportunities for the ever-increasing use of AI in the real world. This has created concerns about the black-box nature of many of the most recent machine learning approaches. In this work, we propose an interpretable neural network that leverages metric and prototype learning for classification tasks. It encodes its own explanations and provides an improved case-based reasoning through learning prototypes in an embedding space learned by a probabilistic nearest neighbor rule. Through experiments, we demonstrated the effectiveness of the proposed method in both performance and the accuracy of the explanations provided.
On the Efficiency of Deep Neural Networks
Authors: Yibin Liang, Yang Yi, Lingjia Liu
Date: 2021-09-29 00:00:00
Tasks: Edge-computing, Efficient Neural Network

The efficiency of neural networks is very important in large-scale deployment scenarios such as mobile applications, internet of things, and edge computing. For given performance requirement, an efficient neural network should use the simplest network architecture with minimal number of parameters and connections. In this paper, we discuss several key issues and a new procedure for obtaining efficient networks that minimize total number of parameters and computation requirement. Our first contribution is identifying and analyzing several key components in training efficient networks with the backpropagation (BP) algorithm: 1) softmax normalization in output layers may be one major cause of parameter explosion; 2) using log likelihood ratio (LLR) representation in output layers can reduce overfitting; 3) weight decaying and structural regularization can effectively reduce overfitting when ReLU activation is used. The second contribution is discovering that a well-trained network without overfitting can be effectively pruned using a simple snapshot-based procedure -- after pruning unimportant weights and connections, simply adjust remaining non-weight parameters using the BP algorithm. The snapshot-based pruning method could also be used to evaluate and analyze the efficiency of neural networks. Finally, we hypothesize that there exist lower-bounds of total number of bits for representing parameters and connections with regard to performance metrics for a given optimization problem. Rather than focusing on improving the sole accuracy metric with more complex network architectures, we should also explore the trade-offs between accuracy and total number of representation bits when comparing different network architectures and implementations.
RankedDrop: Enhancing Deep Graph Convolutional Networks Training
Authors: Quentin Petit, Chong Li, Kelun Chai, Serge G Petiton
Date: 2021-09-29 00:00:00

Graph Neural Networks (GNNs) are playing a more and more important role for analyzing unstructured data from the complex real world. Introducing random edge dropping from the input graph at training epochs could reduce over-fitting and over-smoothing phenomenon and increase the depth of GNNs. However, such method relies strongly on the chosen randomness. It makes the accuracy depend on the initialization of the randomness, which let the selection of hyper-parameters be even more difficult. We propose in this paper RankedDrop a novel method with a spatial-aware dropping-edge selection. The selection takes account of graph global information using PageRank, and graph local neighborhood information with node degree. RankedDrop provides a more stable training results comparing to the state-of-the-art solution, by maintaining the advantages of random edge dropping. Furthermore, RankedDrop is a general method that can be deployed on a deep learning framework for enhancing performance of GNNs.
Zero-Shot Coordination via Semantic Relationships Between Actions and Observations
Authors: Mingwei Ma, Jizhou Liu, Samuel Sokota, Max Kleiman-Weiner, Jakob Nicolaus Foerster
Date: 2021-09-29 00:00:00
Tasks: Diagnostic, Inductive Bias

An unaddressed challenge in zero-shot coordination is to take advantage of the semantic relationship between the features of an action and the features of observations. Humans take advantage of these relationships in highly intuitive ways. For instance in the absence of a shared-language, we might point to the object we desire or hold up fingers to indicate how many objects we want. To address this challenge, we investigate the effect of network architecture on the propensity of learning algorithms to make use of these relationships in human-compatible ways. We find that attention-based architectures that jointly process a featurized representation of the observation and the action, have a better inductive bias for exploiting semantic relationships for zero-shot coordination. Excitingly, in a set of diagnostic tasks, these agents produce highly human-compatible policies, without requiring the symmetry relationships of the problems to be hard-coded.
Graph Similarities and Dual Approach for Sequential Text-to-Image Retrieval
Authors: Keonwoo Kim, Sihyeon Jo, Seong-Woo Kim
Date: 2021-09-29 00:00:00
Tasks: Graph Embedding, Image Retrieval, Retrieval, Sentence, Video Captioning, Visual Storytelling

Sequential text-to-image retrieval, a.k.a. Story-to-images task, requires semantic alignment with a given story and maintaining global coherence in drawn image sequence simultaneously. Most of the previous works have only focused on modeling how to follow the content of a given story faithfully. This kind of overfitting tendency hinders matching structural similarity between images, causing an inconsistency in global visual information such as backgrounds. To handle this imbalanced problem, we propose a novel image sequence retrieval framework that utilizes scene graph similarities of the images and a dual learning scheme. Scene graph describes high-level information of visual groundings and adjacency relations of the key entities in a visual scene. In our proposed retriever, the graph encoding head learns to maximize graph embedding similarities among sampled images, giving a strong signal that forces the retriever to also consider morphological relevance with previously sampled images. We set a video captioning as a dual learning task that reconstructs the input story from the sampled image sequence. This inverse mapping gives informative feedback for our proposed retrieval system to maintain global contextual information of a given story. We also suggest a new contextual sentence encoding architecture to embed a sentence in consideration of the surrounding context. Through extensive experiments, Our proposed framework shows better qualitative and quantitative performance with Visual Storytelling benchmark compared to conventional story-to-image models.
Source-Target Unified Knowledge Distillation for Memory-Efficient Federated Domain Adaptation on Edge Devices
Authors: Xiaochen Zhou, Yuchuan Tian, Xudong Wang
Date: 2021-09-29 00:00:00
Tasks: Domain Adaptation, Knowledge Distillation

To support local inference on an edge device, it is necessary to deploy a compact machine learning model on such a device.
When such a compact model is applied to a new environment, its inference accuracy can be degraded if the target data from the new environment have a different distribution from the source data that are used for model training.
To ensure high inference accuracy in the new environment, it is indispensable to adapt the compact model to the target data.
However, to protect users' privacy, the target data cannot be sent to a centralized server for joint training with the source data. Furthermore, utilizing the target data to directly train the compact model cannot achieve sufficient inference accuracy due to its low model capacity.
To this end, a scheme called source-target unified knowledge distillation (STU-KD) is developed in this paper. It starts with a large pretrained model loaded onto the edge device, and then the knowledge of the large model is transferred to the compact model via knowledge distillation.
Since training the large model leads to large memory consumption, a domain adaptation method called lite-residual hypothesis transfer is designed to achieve memory-efficient adaptation to the target data on the edge device. Moreover, to prevent the compact model from forgetting the knowledge of the source data during knowledge distillation, a collaborative knowledge distillation (Co-KD) method is developed to unify the source data on the server and the target data on the edge device to train the compact model. STU-KD can be easily integrated with secure aggregation so that the server cannot obtain the true model parameters of the compact model. Extensive experiments conducted upon several objective recognition tasks show that STU-KD can improve the inference accuracy by up to $14.7\%$, as compared to the state-of-the-art schemes. Results also reveal that the inference accuracy of the compact model is not impacted by incorporating secure aggregation into STU-KD.