pwc-archive/papers-with-abstracts huggingface dataset:
Federated Learning with Decoupled Probabilistic-Weighted Gradient Aggregation
Authors: Jian-hui Duan, Wenzhong Li, Sanglu Lu
Date: 2021-01-01 00:00:00
Tasks: Federated Learning, Variational Inference

 In the federated learning paradigm, multiple mobile clients train local models independently based on datasets generated by edge devices, and the server aggregates parameters/gradients from local models to form a global model.   However, existing model aggregation approaches suffer form high bias on both data distribution   and parameter distribution for non-IID datasets,    which result in severe accuracy drop for increasing number of heterogeneous clients.    In this paper, we proposed a novel decoupled probabilistic-weighted gradient aggregation approach called FeDEC for federated learning.  The key idea is to optimize gradient parameters and statistical parameters in a decoupled way, and aggregate the parameters from local models with probabilistic weights to deal with the heterogeneity of clients.  Since the overall dataset is unaccessible by the central server,   we introduce a variational inference method to derive the optimal probabilistic weights to minimize statistical bias.   We further prove the convergence bound of the proposed approach.   Extensive experiments using mainstream convolutional neural network models based on three federated datasets  show that FeDEC significantly outperforms the state-of-the-arts in terms of model accuracy and training efficiency.
Contextual Graph Reasoning Networks
Authors: Zhaoqing Wang, Jiaming Liu, Yangyuxuan Kang, Mingming Gong, Chuang Zhang, Ming Lu, Ming Wu
Date: 2021-01-01 00:00:00
Tasks: 2D Human Pose Estimation, Instance Segmentation, Pose Estimation, Relation, Segmentation, Semantic Segmentation

Graph Reasoning has shown great potential recently in modeling long-range dependencies, which are crucial for various computer vision tasks. However, the graph representation learned by existing methods is not effective enough as the relation between feature and graph is under-explored. In this work, we propose a novel method named Contextual Graph Reasoning (CGR) that learns a context-aware relation between feature and graph. This is achieved by constructing the projection matrix based on a global set of descriptors during graph projection, and calibrating the evolved graph based on the self-attention of all nodes during graph reprojection. Therefore, contextual information is well explored in both graph projection and reprojection with our method. To verify the effectiveness of our method, we conduct extensive experiments on semantic segmentation, instance segmentation, and 2D human pose estimation. Our method consistently achieves remarkable improvements over state-of-the-art methods, demonstrating the effectiveness and generalization ability of our method.
Continual Memory: Can We Reason After Long-Term Memorization?
Authors: Zhu Zhang, Chang Zhou, Zhou Zhao, Zhijie Lin, Jingren Zhou, Hongxia Yang
Date: 2021-01-01 00:00:00
Tasks: Memorization

Existing reasoning tasks often follow the setting of "reasoning while experiencing", which has an important assumption that the raw contents can be always accessed while reasoning. However, human beings frequently adopt another reasoning setting in daily life, referred to "reasoning after memorizing". Concretely, human beings have the ability to unconsciously memorize his/her experiences within limited memory capacity, from which they can recall and respond to subsequent tasks. In this setting, the raw contents are no longer available during reasoning, thus we need to compress and memorize the input stream in one pass, trying to answer general queries that are unseen before. Memory augmented neural networks introduce a write-read memory to perform such human-like memorization and reasoning, but they continually update the memory from current information and inevitably forget the early contents, failing to answer the queries relevant to early information.  In this paper, we propose the Continual Memory (CM) to explore this ability of reasoning after long-term memorization.  To alleviate the gradual forgetting of early information, we develop self-supervised memorization training with item-level and sequence-level objectives. We demonstrate several interesting characteristics of our continual memory via synthetic data, and evaluate its performance by several downstream tasks, including long-term text QA, long-term video QA and recommendation with long sequences.
Noise-Robust Contrastive Learning
Authors: Junnan Li, Caiming Xiong, Steven Hoi
Date: 2021-01-01 00:00:00
Tasks: Contrastive Learning

Learning from noisy data has attracted much attention, where most methods focus on label noise. In this work, we propose a new framework which simultaneously addresses three types of noise commonly seen in real-world data: label noise, out-of-distribution input, and input corruption. In contrast to most existing methods, we combat noise by learning robust representation. Specifically, we embed images into a low-dimensional subspace by training an autoencoder on the deep features. We regularize the geometric structure of the subspace with robust contrastive learning, which includes an unsupervised consistency loss and a supervised mixup prototypical loss. Furthermore, we leverage the structure of the learned subspace for noise cleaning, by aggregating information from neighboring samples. Experiments on multiple benchmarks demonstrate state-of-the-art performance of our method and robustness of the learned representation. Our code will be released.
Effective Subspace Indexing via Interpolation on Stiefel and Grassmann manifolds
Authors: Wenqing Hu, Tiefeng Jiang, Zhu Li
Date: 2021-01-01 00:00:00

We propose a novel local Subspace Indexing Model with Interpolation (SIM-I) for low-dimensional embedding of image datasets. Our SIM-I is constructed via two steps: in the first step we build a piece-wise linear affinity-aware subspace model under a given partition of the dataset; in the second step we interpolate between several adjacent linear subspace models constructed previously using the `"center of mass" calculation on Stiefel and Grassmann manifolds. The resulting subspace indexing model built by SIM-I is a globally non-linear low-dimensional embedding of the original data set. Furthermore, the interpolation step produces a `"smoothed” version of the piece-wise linear embedding mapping constructed in the first step, and can be viewed as a regularization procedure. We provide experimental results validating the effectiveness of SIM-I, that improves PCA recovery for SIFT dataset and nearest-neighbor classification success rates for MNIST and CIFAR-10 datasets.
Towards Robust Graph Neural Networks against Label Noise
Authors: Jun Xia, Haitao Lin, Yongjie Xu, Lirong Wu, Zhangyang Gao, Siyuan Li, Stan Z. Li
Date: 2021-01-01 00:00:00
Tasks: Attribute, Learning with noisy labels, Meta-Learning, Node Classification, Pseudo Label

Massive labeled data have been used in training deep neural networks, thus label noise has become an important issue therein.  Although learning with noisy labels has made great progress on image datasets in recent years, it has not yet been studied in connection with graph node classification task. In this paper, we proposed a method, named LPM,  to address the problem using Label Propagation (LP) and Meta learning.  Different from previous methods designed for image datasets, our method is based on a special attribute (label smoothness) of graph data, i.e., neighboring nodes in a graph tend to have the same label.  A pseudo label is computed from the neighboring labels for each node in the training set using LP; meta learning is utilized to learn a proper aggregation of the original and pseudo label as the final label.  Experimental results demonstrate that LPM outperforms state-of-the-art methods in graph node classification task with both synthetic and real-world label noise. Source code to reproduce all results will be released.
To Learn Effective Features: Understanding the Task-Specific Adaptation of MAML
Authors: Zhijie Lin, Zhou Zhao, Zhu Zhang, Huai Baoxing, Jing Yuan
Date: 2021-01-01 00:00:00
Tasks: Contrastive Learning, Meta-Learning

Meta learning, an effective way for learning unseen tasks with few samples, is an important research
area in machine learning.
Model Agnostic Meta-Learning~(MAML)~(\cite{finn2017model}) is one of the most well-known gradient-based meta learning algorithms, that learns
the meta-initialization through the inner and outer optimization loop.
The inner loop is to perform fast adaptation in several gradient update steps with the support datapoints, 
while the outer loop to generalize the updated model to the query datapoints.
Recently, it has been argued that instead of rapid learning and adaptation, the learned meta-initialization through MAML
has already absorbed the high-quality features prior, where the task-specific head at training 
facilitates the feature learning.
In this work, we investigate the impact of the task-specific adaptation of MAML and discuss the general formula for
other gradient-based and metric-based meta-learning approaches.
From our analysis, we further devise the Random Decision Planes~(RDP) algorithm to find a suitable linear classifier
without any gradient descent step and the Meta Contrastive Learning~(MCL) algorithm to exploit the inter-samples relationship
instead of the expensive inner-loop adaptation. 
We conduct sufficient experiments on various datasets to explore our proposed algorithms.
Semantic Inference Network for Few-shot Streaming Label Learning
Authors: Zhen Wang, Liu Liu, Yiqun Duan, DaCheng Tao
Date: 2021-01-01 00:00:00
Tasks: Meta-Learning, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Streaming label learning aims to model newly emerged labels for multi-label classification systems, which requires plenty of new label data for training. However, in changing environments, only a small amount of new label data can practically be collected. In this work, we formulate and study few-shot streaming label learning (FSLL), which models emerging new labels with only a few annotated examples by utilizing the knowledge learned from past labels. We propose a meta-learning framework, Semantic Inference Network (SIN), which can learn and infer the semantic correlation between new labels and past labels to adapt FSLL tasks from a few examples effectively. SIN leverages label semantic representation to regularize the output space and acquires label-wise meta-knowledge based on the gradient-based meta-learning. Moreover, SIN incorporates a novel label decision module with a meta-threshold loss to find the optimal confidence thresholds for each new label. Theoretically, we demonstrate that the proposed semantic inference mechanism could constrain the complexity of hypotheses space to reduce the risk of overfitting and achieve better generalizability. Experimentally, extensive empirical results and ablation studies illustrate the superior performance of SIN over the prior state-of-the-art methods on FSLL.
Improved Uncertainty Post-Calibration via Rank Preserving Transforms
Authors: Yu Bai, Tengyu Ma, Huan Wang, Caiming Xiong
Date: 2021-01-01 00:00:00
Tasks: text-classification, Text Classification

Modern machine learning models with high accuracy often exhibit poor uncertainty calibration: the output probabilities of the model do not reflect its accuracy, and tend to be over-confident. Existing post-calibration methods such as temperature scaling recalibrate a trained model using rather simple calibrators with one or few parameters, which can have a rather limited capacity. In this paper, we propose Neural Rank Preserving Transforms (NRPT), a new post-calibration method that adjusts the output probabilities of a trained classifier using a calibrator of higher capacity, while maintaining its prediction accuracy. NRPT learns a calibrator that preserves the rank of the probabilities through general monotonic transforms, individualizes to the original input, and allows learning with any loss function that encourages calibration. We show experimentally that NRPT improves the expected calibration error (ECE) significantly over existing post-calibration methods such as (local) temperature scaling on large-scale image and text classification tasks. The performance of NRPT can further match ensemble methods such as deep ensembles, while being much more parameter-efficient. We further demonstrate the improved calibration ability of NRPT beyond the ECE metric, such as accuracy among top-confidence predictions, as well as optimizing the tradeoff between calibration and sharpness.
USING OBJECT-FOCUSED IMAGES AS AN IMAGE AUGMENTATION TECHNIQUE TO IMPROVE THE ACCURACY OF IMAGE-CLASSIFICATION MODELS WHEN VERY LIMITED DATA SETS ARE AVAILABLE
Authors: Ahmad Melhem Hammoud, Ahmad Rabih Ghandour
Date: 2021-01-01 00:00:00
Tasks: Image Augmentation, image-classification, Image Classification

Today, many of the machine learning models are extremely data hungry. On the other hand, the accuracy of the algorithms used is very often affected by the amount of the training data available, which is, unfortunately, rarely abundant. Fortunately, image augmentation is one of the very powerful techniques that can be used by computer-vision engineers to expand their existing image data sets. This paper presents an innovative way for creating a variation of existing images and introduces the idea of using an Object-Focused Image (OFI). This is when an image includes only the labeled object and everything else is made transparent. The objective of OFI method is to expand the existing image data set and hence improve the accuracy of the model used to classify images. This paper also elaborates on the OFI approach and compares the accuracy of five different models with the same network design and settings but with different content of the training data set. The experiments presented in this paper show that using OFIs along with the original images can lead to an increase in the validation accuracy of the used model. In fact, when the OFI technique is used, the number of the images supplied nearly doubles.
Robust Learning for Congestion-Aware Routing
Authors: Sreenivas Gollapudi, Kostas Kollias, Benjamin Plaut, Ameya Velingker
Date: 2021-01-01 00:00:00
Tasks: valid

We consider the problem of routing users through a network with unknown congestion functions over an infinite time horizon. On each time step $t$, the algorithm receives a routing request and must select a valid path. For each edge $e$ in the selected path, the algorithm incurs a cost $c_e^t = f_e(x_e^t) + \eta_e^t$, where $x_e^t$ is the flow on edge $e$ at time $t$, $f_e$ is the congestion function, and $\eta_e^t$ is a noise sample drawn from an unknown distribution. The algorithm observes $c_e^t$, and can use this observation in future routing decisions. The routing requests are supplied adversarially. 

We present an algorithm with cumulative regret $\tilde{O}(|E| t^{2/3})$, where the regret on each time step is defined as the difference between the total cost incurred by our chosen path and the minimum cost among all valid paths. Our algorithm has space complexity $O(|E| t^{1/3})$ and time complexity $O(|E| \log t)$. We also validate our algorithm empirically using graphs from New York City road networks.
Recurrent Exploration Networks for Recommender Systems
Authors: Hao Wang, Yifei Ma, Hao Ding, Bernie Wang
Date: 2021-01-01 00:00:00
Tasks: Recommendation Systems, Representation Learning

Recurrent neural networks have proven effective in modeling sequential user feedbacks for recommender systems. However, they usually focus solely on item relevance and fail to effectively explore diverse items for users, therefore harming the system performance in the long run. To address this problem, we propose a new type of recurrent neural networks, dubbed recurrent exploration networks (REN), to jointly perform representation learning and effective exploration in the latent space. REN tries to balance relevance and exploration while taking into account the uncertainty in the representations. Our theoretical analysis shows that REN can preserve the rate-optimal sublinear regret (Chu et al., 2011) even when there exists uncertainty in the learned representations. Our empirical study demonstrates that REN can achieve satisfactory long-term rewards on both synthetic and real-world recommendation datasets, outperforming state-of-the-art models.
Task Calibration for Distributional Uncertainty in Few-Shot Classification
Authors: Sungnyun Kim, Se-Young Yun
Date: 2021-01-01 00:00:00
Tasks: Classification, General Classification, Meta-Learning

As numerous meta-learning algorithms improve performance when solving few-shot classification problems for practical applications, accurate prediction of uncertainty, though challenging, has been considered essential. Recent works have focused on Bayesian methods, but they do not handle complex and high-dimensional data sufficiently well. In this study, we contemplate modeling uncertainty in a few-shot classification framework and propose a straightforward method that appropriately predicts task uncertainty. Specifically, we measure the distributional mismatch between support and query sets via class-wise similarities. We emphasize our model's ability to apply uncertainty estimation without relying on posterior approximation unlike the Bayesian methods. Moreover, our method is readily expanded to include a range of meta-learning models. Through extensive experiments including dataset shift, we present that our training strategy helps the model avoid being indiscriminately confident, and thereby, produce calibrated classification results without the loss of accuracy.
Layer-wise Adversarial Defense: An ODE Perspective
Authors: Zonghan Yang, Yang Liu, Chenglong Bao, Zuoqiang Shi
Date: 2021-01-01 00:00:00
Tasks: Adversarial Defense

Deep neural networks are observed to be fragile against adversarial attacks, which have dramatically limited their practical applicability. On improving model robustness, the adversarial training techniques have proven effective and gained increasing attention from research communities. Existing adversarial training approaches mainly focus on perturbations to inputs, while the effect of the perturbations in hidden layers remains underexplored. In this work, we propose layer-wise adversarial defense which improves adversarial training by a noticeable margin. The basic idea of our method is to strengthen all of the hidden layers with perturbations that are proportional to the back-propagated gradients. In order to study the layer-wise neural dynamics, we formulate our approach from the perspective of ordinary differential equations (ODEs) and build up its extended relationship with conventional adversarial training methods, which tightens the relationship between neural networks and ODEs. In the implementation, we propose two different training algorithms by discretizing the ODE model with the Lie-Trotter and the Strang-Marchuk splitting schemes from the operator-splitting theory. Experiments on CIFAR-10 and CIFAR-100 benchmarks show that our methods consistently improve adversarial model robustness on top of widely-used strong adversarial training techniques.
Rethinking the Pruning Criteria for Convolutional Neural Network
Authors: Zhongzhan Huang, Xinjiang Wang, Ping Luo
Date: 2021-01-01 00:00:00
Proceeding: NeurIPS 2021 12

Channel pruning is a popular technique for compressing convolutional neural networks (CNNs), and various pruning criteria have been proposed to remove the redundant filters of CNNs. From our comprehensive experiments, we found some blind spots on pruning criteria: (1) Similarity: There are some strong similarities among several primary pruning criteria that are widely cited and compared. The ranks of filters’  importance in a convolutional layer according to these criteria are almost the same, resulting in similar pruned structures. (2) Applicability: For a large network (each convolutional layer has a large number of filters), these criteria can not distinguish the network redundancy well from their measured filters'  importance. In this paper, we theoretically validate these two findings with our assumption that the well-trained convolutional filters in each layer approximately follow a Gaussian-alike distribution. This assumption is verified through systematic and extensive statistical tests.
Improving Machine Translation by Searching Skip Connections Efficiently
Authors: Chen Yang, Houfeng Wang
Date: 2021-01-01 00:00:00
Tasks: Machine Translation, Translation

As a widely used neural network model in NLP (neural language processing), transformer model achieves state-of-the-art performance in several translation tasks. Transformer model has a fixed skip connection architecture among different layers. However, the influence of other possible skip connection architectures are not discussed completely in transformer model. We search different architectures of skip connection to discover better architectures in different datasets. To improve the efficiency of trying different skip connection architectures, we apply the idea of network morphism to add skip connections as a procedure of fine-tuning. Our fine-tuning method outperforms the best models trained by the same or smaller datasets in WMT'16 En-De, WMT'14 En-Fr and WMT'18 En-De with 226M back-translation sentences. We also make experiment on transferring searched skip connection architectures to new transformer models.
UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers
Authors: Siyi Hu, Fengda Zhu, Xiaojun Chang, Xiaodan Liang
Date: 2021-01-01 00:00:00
Proceeding: ICLR 2021 1
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), SMAC, SMAC+

Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task. The limitation is due to the restricted model architecture related to fixed input and output dimensions. This hinders the experience accumulation and transfer of the learned agent over tasks with diverse levels of difficulty (e.g. 3 vs 3 or 5 vs 6 multi-agent games).  In this paper, we make the first attempt to explore a universal multi-agent reinforcement learning pipeline, designing one single architecture to fit tasks with the requirement of different observation and action configurations. Unlike previous RNN-based models, we utilize a transformer-based model to generate a flexible policy by decoupling the policy distribution from the intertwined input observation with an importance weight measured by the merits of the self-attention mechanism. Compared to a standard transformer block, the proposed model, named as Universal Policy Decoupling Transformer (UPDeT), further relaxes the action restriction and makes the multi-agent task's decision process more explainable. UPDeT is general enough to be plugged into any multi-agent reinforcement learning pipeline and equip them with strong generalization abilities that enables the handling of multiple tasks at a time. Extensive experiments on large-scale SMAC multi-agent competitive games demonstrate that the proposed UPDeT-based multi-agent reinforcement learning achieves significant results relative to state-of-the-art approaches, demonstrating advantageous transfer capability in terms of both performance and training speed (10 times faster).