pwc-archive/papers-with-abstracts huggingface dataset:
NTopo: Mesh-free Topology Optimization using Implicit Neural Representations
Authors: Jonas Zehnder, Yue Li, Stelian Coros, Bernhard Thomaszewski
Date: 2021-02-22 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Self-Supervised Learning

Recent advances in implicit neural representations show great promise when it comes to generating numerical solutions to partial differential equations. Compared to conventional alternatives, such representations employ parameterized neural networks to define, in a mesh-free manner, signals that are highly-detailed, continuous, and fully differentiable. In this work, we present a novel machine learning approach for topology optimization -- an important class of inverse problems with high-dimensional parameter spaces and highly nonlinear objective landscapes. To effectively leverage neural representations in the context of mesh-free topology optimization, we use multilayer perceptrons to parameterize both density and displacement fields. Our experiments indicate that our method is highly competitive for minimizing structural compliance objectives, and it enables self-supervised learning of continuous solution spaces for topology optimization problems.
Multi-View Feature Representation for Dialogue Generation with Bidirectional Distillation
Authors: Shaoxiong Feng, Xuancheng Ren, Kan Li, Xu sun
Date: 2021-02-22 00:00:00
Tasks: Dialogue Generation, General Knowledge, Knowledge Distillation

Neural dialogue models suffer from low-quality responses when interacted in practice, demonstrating difficulty in generalization beyond training data. Recently, knowledge distillation has been used to successfully regularize the student by transferring knowledge from the teacher. However, the teacher and the student are trained on the same dataset and tend to learn similar feature representations, whereas the most general knowledge should be found through differences. The finding of general knowledge is further hindered by the unidirectional distillation, as the student should obey the teacher and may discard some knowledge that is truly general but refuted by the teacher. To this end, we propose a novel training framework, where the learning of general knowledge is more in line with the idea of reaching consensus, i.e., finding common knowledge that is beneficial to different yet all datasets through diversified learning partners. Concretely, the training task is divided into a group of subtasks with the same number of students. Each student assigned to one subtask not only is optimized on the allocated subtask but also imitates multi-view feature representation aggregated from other students (i.e., student peers), which induces students to capture common knowledge among different subtasks and alleviates the over-fitting of students on the allocated subtasks. To further enhance generalization, we extend the unidirectional distillation to the bidirectional distillation that encourages the student and its student peers to co-evolve by exchanging complementary knowledge with each other. Empirical results and analysis demonstrate that our training framework effectively improves the model generalization without sacrificing training efficiency.
PCB-Fire: Automated Classification and Fault Detection in PCB
Authors: Tejas Khare, Vaibhav Bahel, Anuradha C. Phadke
Date: 2021-02-22 00:00:00
Tasks: Classification, Fault Detection, General Classification, object-detection, Object Detection

Printed Circuit Boards are the foundation for the functioning of any electronic device, and therefore are an essential component for various industries such as automobile, communication, computation, etc. However, one of the challenges faced by the PCB manufacturers in the process of manufacturing of the PCBs is the faulty placement of its components including missing components. In the present scenario the infrastructure required to ensure adequate quality of the PCB requires a lot of time and effort. The authors present a novel solution for detecting missing components and classifying them in a resourceful manner. The presented algorithm focuses on pixel theory and object detection, which has been used in combination to optimize the results from the given dataset.
Weighted Graph Nodes Clustering via Gumbel Softmax
Authors: Deepak Bhaskar Acharya, Huaming Zhang
Date: 2021-02-22 00:00:00
Tasks: Clustering, Graph Clustering, Node Clustering, Recommendation Systems

Graph is a ubiquitous data structure in data science that is widely applied in social networks, knowledge representation graphs, recommendation systems, etc. When given a graph dataset consisting of one graph or more graphs, where the graphs are weighted in general, the first step is often to find clusters in the graphs. In this paper, we present some ongoing research results on graph clustering algorithms for clustering weighted graph datasets, which we name as Weighted Graph Node Clustering via Gumbel Softmax (WGCGS for short). We apply WGCGS on the Karate club weighted network dataset. Our experiments demonstrate that WGCGS can efficiently and effectively find clusters in the Karate club weighted network dataset. Our algorithm's effectiveness is demonstrated by (1) comparing the clustering result obtained from our algorithm and the given labels of the dataset; and (2) comparing various metrics between our clustering algorithm and other state-of-the-art graph clustering algorithms.
Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning
Authors: Lanqing Li, Yuanhao Huang, Mingzhe Chen, Siteng Luo, Dijun Luo, Junzhou Huang
Date: 2021-02-22 00:00:00
Tasks: Contrastive Learning, Meta-Learning, reinforcement-learning, Reinforcement Learning (RL), Representation Learning

Meta-learning for offline reinforcement learning (OMRL) is an understudied problem with tremendous potential impact by enabling RL algorithms in many real-world applications. A popular solution to the problem is to infer task identity as augmented state using a context-based encoder, for which efficient learning of robust task representations remains an open challenge. In this work, we provably improve upon one of the SOTA OMRL algorithms, FOCAL, by incorporating intra-task attention mechanism and inter-task contrastive learning objectives, to robustify task representation learning against sparse reward and distribution shift. Theoretical analysis and experiments are presented to demonstrate the superior performance and robustness of our end-to-end and model-free framework compared to prior algorithms across multiple meta-RL benchmarks.
Slowly Varying Regression under Sparsity
Authors: Dimitris Bertsimas, Vassilis Digalakis Jr, Michael Linghzi Li, Omar Skali Lami
Date: 2021-02-22 00:00:00
Tasks: parameter estimation, regression

We present the framework of slowly varying regression under sparsity, allowing sparse regression models to exhibit slow and sparse variations. The problem of parameter estimation is formulated as a mixed-integer optimization problem. We demonstrate that it can be precisely reformulated as a binary convex optimization problem through a novel relaxation technique. This relaxation involves a new equality on Moore-Penrose inverses, convexifying the non-convex objective function while matching the original objective on all feasible binary points. This enables us to efficiently solve the problem to provable optimality using a cutting plane-type algorithm. We develop a highly optimized implementation of this algorithm, substantially improving upon the asymptotic computational complexity of a straightforward implementation. Additionally, we propose a fast heuristic method that guarantees a feasible solution and, as empirically illustrated, produces high-quality warm-start solutions for the binary optimization problem. To tune the framework's hyperparameters, we suggest a practical procedure relying on binary search that, under certain assumptions, is guaranteed to recover the true model parameters. On both synthetic and real-world datasets, we demonstrate that the resulting algorithm outperforms competing formulations in comparable times across various metrics, including estimation accuracy, predictive power, and computational time. The algorithm is highly scalable, allowing us to train models with thousands of parameters. Our implementation is available open-source at https://github.com/vvdigalakis/SSVRegression.git.
UniT: Multimodal Multitask Learning with a Unified Transformer
Authors: Ronghang Hu, Amanpreet Singh
Date: 2021-02-22 00:00:00
Proceeding: ICCV 2021 10
Tasks: Decoder, Multimodal Reasoning, Multi-Task Learning, Natural Language Understanding, object-detection, Object Detection, Visual Reasoning

We propose UniT, a Unified Transformer model to simultaneously learn the most prominent tasks across different domains, ranging from object detection to natural language understanding and multimodal reasoning. Based on the transformer encoder-decoder architecture, our UniT model encodes each input modality with an encoder and makes predictions on each task with a shared decoder over the encoded input representations, followed by task-specific output heads. The entire model is jointly trained end-to-end with losses from each task. Compared to previous efforts on multi-task learning with transformers, we share the same model parameters across all tasks instead of separately fine-tuning task-specific models and handle a much higher variety of tasks across different domains. In our experiments, we learn 7 tasks jointly over 8 datasets, achieving strong performance on each task with significantly fewer parameters. Our code is available in MMF at https://mmf.sh.
Divide-and-conquer methods for big data analysis
Authors: Xueying Chen, Jerry Q. Cheng, Min-ge Xie
Date: 2021-02-22 00:00:00

In the context of big data analysis, the divide-and-conquer methodology refers to a multiple-step process: first splitting a data set into several smaller ones; then analyzing each set separately; finally combining results from each analysis together. This approach is effective in handling large data sets that are unsuitable to be analyzed entirely by a single computer due to limits either from memory storage or computational time. The combined results will provide a statistical inference which is similar to the one from analyzing the entire data set. This article reviews some recently developments of divide-and-conquer methods in a variety of settings, including combining based on parametric, semiparametric and nonparametric models, online sequential updating methods, among others. Theoretical development on the efficiency of the divide-and-conquer methods is also discussed.
MobILE: Model-Based Imitation Learning From Observation Alone
Authors: Rahul Kidambi, Jonathan Chang, Wen Sun
Date: 2021-02-22 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Imitation Learning, model, OpenAI Gym

This paper studies Imitation Learning from Observations alone (ILFO) where the learner is presented with expert demonstrations that consist only of states visited by an expert (without access to actions taken by the expert). We present a provably efficient model-based framework MobILE to solve the ILFO problem. MobILE involves carefully trading off strategic exploration against imitation - this is achieved by integrating the idea of optimism in the face of uncertainty into the distribution matching imitation learning (IL) framework. We provide a unified analysis for MobILE, and demonstrate that MobILE enjoys strong performance guarantees for classes of MDP dynamics that satisfy certain well studied notions of structural complexity. We also show that the ILFO problem is strictly harder than the standard IL problem by presenting an exponential sample complexity separation between IL and ILFO. We complement these theoretical results with experimental simulations on benchmark OpenAI Gym tasks that indicate the efficacy of MobILE. Code for implementing the MobILE framework is available at https://github.com/rahulkidambi/MobILE-NeurIPS2021.
Post-hoc Overall Survival Time Prediction from Brain MRI
Authors: Renato Hermoza, Gabriel Maicas, Jacinto C. Nascimento, Gustavo Carneiro
Date: 2021-02-22 00:00:00
Tasks: Brain Tumor Segmentation, Prediction, Prognosis, Segmentation, Tumor Segmentation

Overall survival (OS) time prediction is one of the most common estimates of the prognosis of gliomas and is used to design an appropriate treatment planning. State-of-the-art (SOTA) methods for OS time prediction follow a pre-hoc approach that require computing the segmentation map of the glioma tumor sub-regions (necrotic, edema tumor, enhancing tumor) for estimating OS time. However, the training of the segmentation methods require ground truth segmentation labels which are tedious and expensive to obtain. Given that most of the large-scale data sets available from hospitals are unlikely to contain such precise segmentation, those SOTA methods have limited applicability. In this paper, we introduce a new post-hoc method for OS time prediction that does not require segmentation map annotation for training. Our model uses medical image and patient demographics (represented by age) as inputs to estimate the OS time and to estimate a saliency map that localizes the tumor as a way to explain the OS time prediction in a post-hoc manner. It is worth emphasizing that although our model can localize tumors, it uses only the ground truth OS time as training signal, i.e., no segmentation labels are needed. We evaluate our post-hoc method on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2019 data set and show that it achieves competitive results compared to pre-hoc methods with the advantage of not requiring segmentation labels for training.
Unsupervised Meta Learning for One Shot Title Compression in Voice Commerce
Authors: Snehasish Mukherjee
Date: 2021-02-22 00:00:00
Tasks: Meta-Learning

Product title compression for voice and mobile commerce is a well studied problem with several supervised models proposed so far. However these models have 2 major limitations; they are not designed to generate compressions dynamically based on cues at inference time, and they do not transfer well to different categories at test time. To address these shortcomings we model title compression as a meta learning problem where we ask can we learn a title compression model given only 1 example compression? We adopt an unsupervised approach to meta training by proposing an automatic task generation algorithm that models the observed label generation process as the outcome of 4 unobserved processes. We create parameterized approximations to each of these 4 latent processes to get a principled way of generating random compression rules, which are treated as different tasks. For our main meta learner, we use 2 models; M1 and M2. M1 is a task agnostic embedding generator whose output feeds into M2 which is a task specific label generator. We pre-train M1 on a novel unsupervised segment rank prediction task that allows us to treat M1 as a segment generator that also learns to rank segments during the meta-training process. Our experiments on 16000 crowd generated meta-test examples show that our unsupervised meta training regime is able to acquire a learning algorithm for different tasks after seeing only 1 example for each task. Further, we show that our model trained end to end as a black box meta learner, outperforms non parametric approaches. Our best model obtains an F1 score of 0.8412, beating the baseline by a large margin of 25 F1 points.
Hide and Seek: Outwitting Community Detection Algorithms
Authors: Shravika Mittal, Debarka Sengupta, Tanmoy Chakraborty
Date: 2021-02-22 00:00:00
Tasks: Community Detection

Community affiliation of a node plays an important role in determining its contextual position in the network, which may raise privacy concerns when a sensitive node wants to hide its identity in a network. Oftentimes, a target community seeks to protect itself from adversaries so that its constituent members remain hidden inside the network. The current study focuses on hiding such sensitive communities so that the community affiliation of the targeted nodes can be concealed. This leads to the problem of community deception which investigates the avenues of minimally rewiring nodes in a network so that a given target community maximally hides from a community detection algorithm. We formalize the problem of community deception and introduce NEURAL, a novel method that greedily optimizes a node-centric objective function to determine the rewiring strategy. Theoretical settings pose a restriction on the number of strategies that can be employed to optimize the objective function, which in turn reduces the overhead of choosing the best strategy from multiple options. We also show that our objective function is submodular and monotone. When tested on both synthetic and 7 real-world networks, NEURAL is able to deceive 6 widely used community detection algorithms. We benchmark its performance with respect to 4 state-of-the-art methods on 4 evaluation metrics. Additionally, our qualitative analysis of 3 other attributed real-world networks reveals that NEURAL, quite strikingly, captures important meta-information about edges that otherwise could not be inferred by observing only their topological structures.
Self-Supervised Learning of Graph Neural Networks: A Unified Review
Authors: Yaochen Xie, Zhao Xu, Jingtun Zhang, Zhengyang Wang, Shuiwang Ji
Date: 2021-02-22 00:00:00
Tasks: Self-Supervised Learning

Deep models trained in supervised mode have achieved remarkable success on a variety of tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a new paradigm for making use of large amounts of unlabeled samples. SSL has achieved promising performance on natural language and image learning tasks. Recently, there is a trend to extend such success to graph data using graph neural networks (GNNs). In this survey, we provide a unified review of different ways of training GNNs using SSL. Specifically, we categorize SSL methods into contrastive and predictive models. In either category, we provide a unified framework for methods as well as how these methods differ in each component under the framework. Our unified treatment of SSL methods for GNNs sheds light on the similarities and differences of various methods, setting the stage for developing new methods and algorithms. We also summarize different SSL settings and the corresponding datasets used in each setting. To facilitate methodological development and empirical comparison, we develop a standardized testbed for SSL in GNNs, including implementations of common baseline methods, datasets, and evaluation metrics.
Coping with Mistreatment in Fair Algorithms
Authors: Ankit Kulshrestha, Ilya Safro
Date: 2021-02-22 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

Machine learning actively impacts our everyday life in almost all endeavors and domains such as healthcare, finance, and energy. As our dependence on the machine learning increases, it is inevitable that these algorithms will be used to make decisions that will have a direct impact on the society spanning all resolutions from personal choices to world-wide policies. Hence, it is crucial to ensure that (un)intentional bias does not affect the machine learning algorithms especially when they are required to take decisions that may have unintended consequences. Algorithmic fairness techniques have found traction in the machine learning community and many methods and metrics have been proposed to ensure and evaluate fairness in algorithms and data collection. In this paper, we study the algorithmic fairness in a supervised learning setting and examine the effect of optimizing a classifier for the Equal Opportunity metric. We demonstrate that such a classifier has an increased false positive rate across sensitive groups and propose a conceptually simple method to mitigate this bias. We rigorously analyze the proposed method and evaluate it on several real world datasets demonstrating its efficacy.
CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surface
Authors: Hang Liu, Xiaojun Yuan, Ying-Jun Angela Zhang
Date: 2021-02-22 00:00:00
Tasks: image-classification, Image Classification

We study over-the-air model aggregation in federated edge learning (FEEL) systems, where channel state information at the transmitters (CSIT) is assumed to be unavailable. We leverage the reconfigurable intelligent surface (RIS) technology to align the cascaded channel coefficients for CSIT-free model aggregation. To this end, we jointly optimize the RIS and the receiver by minimizing the aggregation error under the channel alignment constraint. We then develop a difference-of-convex algorithm for the resulting non-convex optimization. Numerical experiments on image classification show that the proposed method is able to achieve a similar learning accuracy as the state-of-the-art CSIT-based solution, demonstrating the efficiency of our approach in combating the lack of CSIT.
Feature-level Attentive ICF for Recommendation
Authors: Zhiyong Cheng, Fan Liu, Shenghan Mei, Yangyang Guo, Lei Zhu, Liqiang Nie
Date: 2021-02-22 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Item-based collaborative filtering (ICF) enjoys the advantages of high recommendation accuracy and ease in online penalization and thus is favored by the industrial recommender systems. ICF recommends items to a target user based on their similarities to the previously interacted items of the user. Great progresses have been achieved for ICF in recent years by applying advanced machine learning techniques (e.g., deep neural networks) to learn the item similarity from data. The early methods simply treat all the historical items equally and recently proposed methods attempt to distinguish the different importance of historical items when recommending a target item. Despite the progress, we argue that those ICF models neglect the diverse intents of users on adopting items (e.g., watching a movie because of the director, leading actors, or the visual effects). As a result, they fail to estimate the item similarity on a finer-grained level to predict the user's preference to an item, resulting in sub-optimal recommendation. In this work, we propose a general feature-level attention method for ICF models. The key of our method is to distinguish the importance of different factors when computing the item similarity for a prediction. To demonstrate the effectiveness of our method, we design a light attention neural network to integrate both item-level and feature-level attention for neural ICF models. It is model-agnostic and easy-to-implement. We apply it to two baseline ICF models and evaluate its effectiveness on six public datasets. Extensive experiments show the feature-level attention enhanced models consistently outperform their counterparts, demonstrating the potential of differentiating user intents on the feature-level for ICF recommendation models.