pwc-archive/papers-with-abstracts huggingface dataset:
Taking ROCKET on an efficiency mission: A distributed solution for fast and accurate multivariate time series classification
Authors: Leonardos Pantiskas, Kees Verstoep, Mark Hoogendoorn, Henri Bal
Date: 2021-09-29 00:00:00
Tasks: feature selection, Time Series, Time Series Analysis, Time Series Classification

Nowadays, with the rising number of sensors in sectors such as healthcare and industry, the problem of multivariate time series classification (MTSC) is getting increasingly relevant and is a prime target for machine and deep learning solutions. Their expanding adoption in real-world environments is causing a shift in focus from the pursuit of ever higher prediction accuracy with complex models towards practical, deployable solutions that balance accuracy and parameters such as prediction speed. An MTSC solution that has attracted attention recently is ROCKET, based on random convolutional kernels, both because of its very fast training process and its state-of-the-art accuracy. However, the large number of features it utilizes may be detrimental to inference time. Examining its theoretical background and limitations enables us to address potential drawbacks and present LightWaveS: a distributed solution for accurate MTSC, which is fast both during training and inference. Specifically, utilizing a wavelet scattering transformation of the time series and distributed feature selection, we manage to create a solution which employs just 2,5% of the ROCKET features, while achieving accuracy comparable to recent deep learning solutions. LightWaveS also scales well with more nodes and large numbers of channels. In addition, it can give interpretability into the nature of an MTSC problem and allows for tuning based on expert opinion. We present three versions of our algorithm and their results on training time, accuracy, inference speedup and scalability. We show that we achieve speedup ranging from 8x to 30x compared to ROCKET during inference on an edge device, on datasets with comparable accuracy.
Online Ad Hoc Teamwork under Partial Observability
Authors: Pengjie Gu, Mengchen Zhao, Jianye Hao, Bo An
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4

Autonomous agents often need to work together as a team to accomplish complex cooperative tasks. Due to privacy and other realistic constraints, agents might need to collaborate with previously unknown teammates on the fly. This problem is known as ad hoc teamwork, which remains a core research challenge. Prior works usually rely heavily on strong assumptions like full observability, fixed and predefined teammates' types. This paper relaxes these assumptions with a novel reinforcement learning framework called ODITS, which allows the autonomous agent to adapt to arbitrary teammates in an online fashion. Instead of limiting teammates into a finite set of predefined types, ODITS automatically learns latent variables of teammates' behaviors to infer how to cooperate with new teammates effectively. To overcome partial observability, we introduce an information-based regularizer to derive \emph{proxy} representations of the learned variables from local observations. Extensive experimental results show that ODITS significantly outperforms various baselines in widely used ad hoc teamwork tasks.
A Compositional Approach to Occlusion in Panoptic Segmentation
Authors: Ajit Sarkaar, A. Lynn Abbott
Date: 2021-09-29 00:00:00
Tasks: Image Segmentation, object-detection, Object Detection, Panoptic Segmentation, Segmentation, Semantic Segmentation

This paper concerns image segmentation, with emphasis on correctly classifying objects that are partially occluded. We present a novel approach based on compositional modeling that has proven to be effective at classifying separate instances of foreground objects. We demonstrate the efficacy of the approach by replacing the object detection pipeline in UPSNet with a compositional element that utilizes a mixture of distributions to model parts of objects. We also show extensive experimental results for the  COCO and Cityscapes datasets. The results show an improvement of 2.6 points in panoptic quality for the top “thing” classes of COCO, and a 3.43% increase in overall recall, using standard UPSNet as a baseline. Moreover, we present qualitative results to demonstrate that improved metrics and datasets are needed for proper characterization of panoptic segmentation systems.
TPU-GAN: Learning temporal coherence from dynamic point cloud sequences
Authors: Zijie Li, Tianqin Li, Amir Barati Farimani
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Generative Adversarial Network, point cloud upsampling, Super-Resolution

Point cloud sequence is an important data representation that provides flexible shape and motion information. Prior work demonstrates that incorporating scene flow information into loss can make model learn temporally coherent feature spaces. However, it is prohibitively expensive to acquire point correspondence information across frames in real-world environments. In this work, we propose a super-resolution generative adversarial network (GAN) for upsampling dynamic point cloud sequences, which does not require point correspondence annotation.  Our model, Temporal Point cloud Upsampling GAN (TPU-GAN), can implicitly learn the underlying temporal coherence from point cloud sequence, which in turn guides the generator to produce temporally coherent output. In addition, we propose a learnable masking module to adapt upsampling ratio according to the point distribution. We conduct extensive experiments on point cloud sequences from two different domains: particles in the fluid dynamical system and human action scanned data. The quantitative and qualitative evaluation demonstrates the effectiveness of our method on upsampling tasks as well as learning temporal coherence from irregular point cloud sequences.
A Transferable General-Purpose Predictor for Neural Architecture Search
Authors: Fred X. Han, Fabian Chudak, Keith G Mills, Mohammad Salameh, Parsa Riahi, Jialin Zhang, Wei Lu, Shangling Jui, Di Niu
Date: 2021-09-29 00:00:00
Tasks: Contrastive Learning, Graph Representation Learning, Neural Architecture Search, Representation Learning

Understanding and modelling the performance of neural architectures is key to Neural Architecture Search (NAS). Performance predictors for neural architectures are widely used in low-cost NAS and achieve high ranking correlations between predicted and ground truth performance in several search spaces. However, existing predictors are often designed based on network encodings specific to a predefined search space and are not generalizable across search spaces or to new families of architectures. In this work, we propose a transferable neural predictor for NAS that can generalize across architecture families, by representing any given candidate Convolutional Neural Network with a computation graph that consists of only primitive operators. Further combined with Contrastive Learning, we propose a semi-supervised graph representation learning procedure that is able to leverage both labelled accuracies and unlabeled information of architectures from multiple families to train universal embeddings of computation graphs and the performance predictor. Experiments conducted on three different NAS benchmarks, including NAS-Bench-101, NAS-Bench-201, and NAS-Bench-301, demonstrate that a predictor pre-trained on other families produces superior transferability when applied to a new family of architectures with a completely different design, after fine-tuning on a small amount of data. We then show that when the proposed transferable predictor is used in NAS, it achieves search results that are comparable to the state-of-the-arts on NAS-Bench-101 at a low evaluation cost.
Communicate Then Adapt: An Effective Decentralized Adaptive Method for Deep Training
Authors: Bicheng Ying, Kun Yuan, Yiming Chen, Hanbin Hu, Yingya Zhang, Pan Pan, Wotao Yin
Date: 2021-09-29 00:00:00

Decentralized adaptive gradient methods, in which each node averages only with its neighbors, are critical to save communication and wall-clock training time in deep learning tasks. While different in concrete recursions, existing decentralized adaptive methods share the same algorithm structure: each node scales its gradient with information of the past squared gradients (which is referred to as the adaptive step) before or while it communicates with neighbors. In this paper, we identify the limitation of such adapt-then/while-communicate structure: it will make the developed algorithms highly sensitive to heterogeneous data distributions, and hence deviate their limiting points from the stationary solution. To overcome this limitation, we propose an effective decentralized adaptive method with a communicate-then-adapt structure, in which each node conducts the adaptive step after finishing the neighborhood communications. The new method is theoretically guaranteed to approach to the stationary solution in the non-convex scenario. Experimental results on a variety of CV/NLP  tasks show that our method has a clear superiority to other existing decentralized adaptive methods.
ASAP DML: Deep Metric Learning with Alternating Sets of Alternating Proxies
Authors: Yeti Z. Gürbüz, Oğul Can, A. Aydin Alatan
Date: 2021-09-29 00:00:00
Tasks: Image Retrieval, Metric Learning, Retrieval

Deep metric learning (DML) aims to minimize empirical expected loss of the pairwise intra-/inter- class proximity violations in the embedding image. We relate DML to feasibility problem of finite chance constraints. We show that minimizer of proxy-based DML satisfies certain chance constraints, and that the worst case generalization performance of the proxy-based methods can be characterized by the radius of the smallest ball around a class proxy to cover the entire domain of the corresponding class samples, suggesting multiple proxies per class helps performance. To provide a scalable algorithm as well as exploiting more proxies, we consider the chance constraints implied by the minimizers of proxy-based DML instances and reformulate DML as finding a feasible point in intersection of such constraints, resulting in a problem to be approximately solved by alternating projections. Simply put, we repeatedly train a regularized proxy-based loss and re-initialize the proxies with the embeddings of the deliberately selected new samples. We apply our method with the well-accepted losses and evaluate on four popular benchmark datasets for image retrieval. Outperforming state-of-the-art, the proposed approach consistently improves the performance of the applied losses.
Understanding Generalized Label Smoothing when Learning with Noisy Labels
Authors: Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Yang Liu
Date: 2021-09-29 00:00:00
Tasks: Learning with noisy labels

Label smoothing (LS) is an arising learning paradigm that uses the positively weighted average of both the hard training labels and uniformly distributed soft labels. It was shown that LS serves as a regularizer for training data with hard labels and therefore improves the generalization of the model. Later it was reported LS even helps with improving robustness when learning with noisy labels. However, we observe that the advantage of LS vanishes when we operate in a high label noise regime. Puzzled by the observation, we proceeded to discover that several proposed learning-with-noisy-labels solutions in the literature instead relate more closely to $\textit{negative label smoothing}$ (NLS), which defines as using a negative weight to combine the hard and soft labels! We show that NLS differs substantially from LS in their achieved model confidence. To differentiate the two cases, we will call LS the positive label smoothing (PLS), and this paper unifies PLS and NLS into $\textit{generalized label smoothing}$ (GLS). We provide understandings for the properties of GLS when learning with noisy labels. Among other established properties, we theoretically show NLS is considered more beneficial when the label noise rates are high. We provide extensive experimental results on multiple benchmarks to support our findings too.
Non-convex Optimization for Learning a Fair Predictor under Equalized Loss Fairness Constraint
Authors: Mohammad Mahdi Khalili, Xueru Zhang, Mahed Abroshan, Iman Vakilinia
Date: 2021-09-29 00:00:00
Tasks: Face Recognition, Fairness

Supervised learning models have been increasingly used in various domains such as lending, college admission, natural language processing, face recognition, etc.  These models may inherit pre-existing biases from training datasets and exhibit discrimination against protected social groups. Various fairness notions have been introduced to address fairness issues. In general, finding a fair predictor leads to a constrained optimization problem, and depending on the fairness notion, it may be non-convex. In this work, we focus on Equalized Loss ($\textsf{EL}$), a fairness notion that requires the prediction error/loss to be equalized across different demographic groups. Imposing this constraint to the learning process leads to a non-convex optimization problem even if the loss function is convex. We introduce algorithms that can leverage off-the-shelf convex programming tools and efficiently find the $\textit{global}$ optimum of this non-convex problem. In particular, we first propose the $\mathtt{ELminimizer}$ algorithm, which finds the optimal $\textsf{EL}$ fair predictor by reducing the non-convex optimization problem to a sequence of convex constrained optimizations. We then propose a simple algorithm that is computationally more efficient compared to $\mathtt{ELminimizer}$ and finds a sub-optimal $\textsf{EL}$ fair predictor using $\textit{unconstrained}$ convex programming tools. Experiments on real-world data show the effectiveness of our algorithms.
Understanding the Variance Collapse of SVGD in High Dimensions
Authors: Jimmy Ba, Murat A Erdogdu, Marzyeh Ghassemi, Shengyang Sun, Taiji Suzuki, Denny Wu, Tianzong Zhang
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Computational Efficiency, Vocal Bursts Intensity Prediction

Stein variational gradient descent (SVGD) is a deterministic inference algorithm that evolves a set of particles to fit a target distribution. Despite its computational efficiency, SVGD often underestimates the variance of the target distribution in high dimensions. In this work we attempt to explain the variance collapse in SVGD. On the qualitative side, we compare the SVGD update with gradient descent on the maximum mean discrepancy (MMD) objective; we observe that the variance collapse phenomenon relates to the bias from deterministic updates present in the "driving force" of SVGD, and empirically verify that removal of such bias leads to more accurate variance estimation. On the quantitative side, we demonstrate that the variance collapse of SVGD can be accurately predicted in the proportional asymptotic limit, i.e., when the number of particles $n$ and dimensions $d$ diverge at the same rate. In particular, for learning high-dimensional isotropic Gaussians, we derive the exact equilibrium variance for both SVGD and MMD-descent, under certain near-orthogonality assumption on the converged particles, and confirm that SVGD suffers from the "curse of dimensionality".
Autoregressive Latent Video Prediction with High-Fidelity Image Generator
Authors: Younggyo Seo, Kimin Lee, Fangchen Liu, Stephen James, Pieter Abbeel
Date: 2021-09-29 00:00:00
Tasks: Data Augmentation, Prediction, Video Prediction, Vocal Bursts Intensity Prediction

Video prediction is an important yet challenging problem; burdened with the tasks of generating future frames and learning environment dynamics. Recently, autoregressive latent video models have proved to be a powerful video prediction tool, by separating the video prediction into two sub-problems: pre-training an image generator model, followed by learning an autoregressive prediction model in the latent space of the image generator. However, successfully generating high-fidelity and high-resolution videos has yet to be seen. In this work, we investigate how to train an autoregressive latent video prediction model capable of predicting high-fidelity future frames with minimal modification to existing models, and produce high-resolution (256x256) videos. Specifically, we scale up prior models by employing a high-fidelity image generator (VQ-GAN) with a causal transformer model, and introduce additional techniques of top-$k$ sampling and data augmentation to further improve video prediction quality. Despite the simplicity, the proposed method achieves competitive performance to state-of-the-art approaches on standard video prediction benchmarks with fewer parameters, and enables high-resolution video prediction on complex and large-scale datasets. Videos are available at the anonymized website https://sites.google.com/view/harp-anonymous
Multi-Agent Reinforcement Learning with Shared Resource in Inventory Management
Authors: Mingxiao Feng, Guozi Liu, Li Zhao, Lei Song, Jiang Bian, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu
Date: 2021-09-29 00:00:00
Tasks: Management, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider inventory management (IM) problem for a single store with a large number of SKUs (stock keeping units) in this paper, where we need to make replenishment decisions for each SKU to balance its supply and demand. Each SKU should cooperate with each other to maximize profits, as well as compete for shared resources e.g., warehouse spaces, budget etc. Co-existence of cooperation and competition behaviors makes IM a complicate game, hence IM can be naturally modelled as a multi-agent reinforcement learning (MARL) problem. In IM problem, we find that agents only interact indirectly with each other through some shared resources, e.g., warehouse spaces. To formally model MARL problems with above structure, we propose shared resource stochastic game along with an efficient algorithm to learn policies particularly for a large number of agents. By leveraging shared-resource structure, our method can greatly reduce model complexity and accelerate learning procedure compared with standard MARL algorithms, as shown by extensive experiments.
SPP-RL: State Planning Policy Reinforcement Learning
Authors: Jacek Cyranka, Zuzanna Opała, Jacek Płocharczyk, Mikhail Zanka
Date: 2021-09-29 00:00:00
Tasks: MuJoCo, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce an algorithm for reinforcement learning, in which the actor plans for the next state provided the current state. To communicate the actor output to the environment we incorporate an inverse dynamics control model and train it using supervised learning. 
We train the RL agent using off-policy state-of-the-art reinforcement learning algorithms: DDPG, TD3, and SAC. To guarantee that the target states are physically relevant, the overall learning procedure is formulated as a constrained optimization problem, solved via the classical Lagrangian optimization method. We benchmark the state planning RL approach using a varied set of continuous environments, including standard MuJoCo tasks,  safety-gym level 0 environments, and AntPush. In SPP approach the optimal policy is being searched for in the space of state-state mappings, a considerably larger space than the traditional space of state-action mappings. We report that quite surprisingly SPP implementations attain superior performance to vanilla state-of-the-art off-policy RL algorithms in the tested environments.
Loss Function Learning for Domain Generalization by Implicit Gradient
Authors: Boyan Gao, Henry Gouk, Yongxin Yang, Timothy Hospedales
Date: 2021-09-29 00:00:00
Tasks: Domain Generalization, Meta-Learning

Generalising robustly to distribution shift is a major challenge that is pervasive across most real-world applications of machine learning. A recent study highlighted that many advanced algorithms proposed to tackle such domain generalisation (DG) fail to outperform a properly tuned empirical risk minimisation (ERM) baseline. We take a different approach, and explore the impact of the ERM loss function on out-of-domain generalisation. In particular, we introduce a novel meta-learning approach to loss function search based on implicit gradient. This enables us to discover a general purpose parametric loss function that provides a drop-in replacement for cross-entropy. Our loss can be used in standard training pipelines to efficiently train robust models using any neural architecture on new datasets. The results show that it clearly surpasses cross-entropy, enables simple ERM to outperform significantly more complicated prior DG methods, and provides state-of-the-art performance across a variety of DG benchmarks. Furthermore, unlike most existing DG approaches, our setup applies to the most practical setting of single-source domain generalisation, on which we show significant improvement.
On the benefits of deep RL in accelerated MRI sampling
Authors: Thomas Sanchez, Igor Krawczuk, Volkan Cevher
Date: 2021-09-29 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning (RL)

Deep learning approaches have shown great promise in accelerating magnetic resonance imaging (MRI), by reconstructing high quality images from highly undersampled data. While previous sampling methods relied on heuristics, recent work has improved the state-of-the-art (SotA) with deep reinforcement learning (RL) sampling policies, which promise the possibility of long term planning and adapting to the observations at test time. In this work, we perform a careful reproduction and comparison of SotA RL sampling methods. We find that i) a simple, easy-to-code, greedily trained fixed policy can match or outperform deep RL methods and ii) find and resolve subtle variations in the preprocessing which previously made results incomparable across different works.
Our results cast doubt on the added value of current RL approaches over fixed masks in MRI sampling and highlight the importance of leveraging strong fixed baselines, standardized reporting as well as isolating the source of improvement in a given work via ablations. We conclude with recommendations for the training and evaluation of deep reconstruction and sampling systems for adaptive MRI based on our findings.
Cross-Stage Transformer for Video Learning
Authors: Yuanze Lin, Xun Guo, Yan Lu
Date: 2021-09-29 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Transformer network has been proved efficient in modeling long-range dependencies in video learning. However, videos contain rich contextual information in both spatial and temporal dimensions, e.g., scenes and temporal reasoning. In traditional transformer networks, stacked transformer blocks work in a sequential and independent way, which may lead to the inefficient propagation of such contextual information. To address this problem, we propose a cross-stage transformer paradigm, which allows to fuse self-attentions and features from different blocks. By inserting the proposed cross-stage mechanism in existing spatial and temporal transformer blocks, we build a separable transformer network for video learning based on ViT structure, in which self-attentions and features are progressively aggregated from one block to the next. Extensive experiments show that our approach outperforms existing ViT based video transformer approaches with the same pre-training dataset on mainstream video action recognition datasets of Kinetics-400 (Top-1 accuracy 81.8%) and Kinetics-600 (Top-1 accuracy 84.0%). Due to the effectiveness of cross-stage transformer, our proposed method achieves comparable performance with other ViT based approaches with much lower computation cost (e.g., 8.6% of ViViT’s FLOPs) in inference process. As an independent module, our proposed method can be conveniently added on other video transformer frameworks.