pwc-archive/papers-with-abstracts huggingface dataset:
Chaining Data - A Novel Paradigm in Artificial Intelligence Exemplified with NMF based Clustering
Authors: Norman J Mapes, Sumeet Dua
Date: 2021-09-29 00:00:00
Tasks: Clustering, Community Detection, Multiview Clustering

In the era of artificial intelligence there is an acceleration of high quality inference from the fusion of data and we have overcome the linking challenge associated with higher order features. We have fundamentally linked together tables of databases for clustering algorithms and expect this paradigm and those related to it to produce many new insights. We propose linked view clustering that is an extension of multi-view clustering by adding complementary and consensus information across linked views of each datapoint. While there are many methods, we focus on non-negative matrix factorization combined with the fusion of linking data in a manner that corresponds to extracting knowledge from the multiple tables of a relational database. It is commonplace to identify hashtag communities on social media by word usage, however there exists troves of data not included but could be. We can incorporate locations by hashtag to improve community detection, this is multiNMF or multiview clustering, but we extend this method to beyond the first link. A general artificial intelligence method to incorporate any table that can be chained backwards has not been done before to our knowledge. We call this linked view NMF or chained view clustering and give the algorithms to perform multiplicative updates and the general solution that can be solved using automatic differentiation such as JAX. We demonstrate how the equations can be interpreted on synthetic data as well as how information flows through the links and as a proof of concept on real data we incorporate word vectors using the method on an authorship clustering dataset.
Learning Controllable Elements Oriented Representations for Reinforcement Learning
Authors: Qi Yi, Jiaming Guo, Rui Zhang, Shaohui Peng, Xing Hu, Xishan Zhang, Ke Tang, Zidong Du, Qi Guo, Yunji Chen
Date: 2021-09-29 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

Deep Reinforcement Learning (deep RL) has been successfully applied to solve various decision-making problems in recent years. However, the observations in many real-world tasks are often high dimensional and include much task-irrelevant information, limiting the applications of RL algorithms. To tackle this problem, we propose LCER, a representation learning method that aims to provide RL algorithms with compact and sufficient descriptions of the original observations.  Specifically, LCER trains representations to retain the controllable elements of the environment, which can reflect the action-related environment dynamics and thus are likely to be task-relevant.  We demonstrate the strength of LCER on the DMControl Suite, proving that it can achieve state-of-the-art performance. To the best of our knowledge, LCER is the first representation learning algorithm that enables the pixel-based SAC to outperform state-based SAC on the DMControl 100K benchmark, showing that the obtained representations can match the oracle descriptions ($i.e.$ the physical states) of the environment.
KIMERA: Injecting Domain Knowledge into Vacant Transformer Heads
Authors: Benjamin Winter, Alexei Figueroa, Alexander Löser, Felix Alexander Gers, Amy Siu
Date: 2021-09-29 00:00:00
Proceeding: LREC 2022 6
Tasks: Information Retrieval, Model Compression, Retrieval

Training transformer language models requires vast amounts of text and computational resources. This drastically limits the usage of these models in niche domains for which they are not optimized, or where domain-specific training data is scarce. We focus here on the clinical domain because of its limited access to training data in common tasks, while structured ontological data is often readily available. Recent observations in model compression of transformer models show optimization potential in improving the representation capacity of attention heads. We propose KIMERA (Knowledge Injection via Mask Enforced Retraining of Attention) for detecting, retraining and instilling attention heads with complementary structured domain knowledge. 
Our novel multi-task training scheme effectively identifies and targets individual attention heads that are least useful for a given downstream task and optimizes their representation with information from structured data. 
Due to its multi-task nature KIMERA generalizes well, thereby building the basis for an efficient fine-tuning.
KIMERA achieves significant performance boosts on seven datasets in the medical domain in Information Retrieval and Clinical Outcome Prediction settings. We apply KIMERA to BERT-base to evaluate the extent of the domain transfer and also improve on the already strong results of BioBERT in the clinical domain.
Conjugation Invariant Learning with Neural Networks
Authors: Aaron Yi Rui Low, Subhroshekhar Ghosh, Yong Sheng Soh
Date: 2021-09-29 00:00:00
Tasks: Multi-Object Tracking, Object Tracking

Machine learning under the constraint of symmetries, given by group invariances or equivariances, has emerged as a topic of active interest in recent years. Natural settings for such applications include the multi-reference alignment and cryo electron microscopy, multi-object tracking, spherical images, and so on. A fundamental paradigm among such symmetries is the action of a group by symmetries, which often pertains to change of basis or relabelling of objects in pure and applied mathematics. Thus, a naturally significant class of functions consists of those that are intrinsic to the problem, in the sense of being independent of such base change or relabelling; in other words invariant under the conjugation action by a group. In this work, we investigate such functions, known as class functions, leveraging tools from group representation theory. A fundamental ingredient in our approach are given by the so-called irreducible characters of the group, which are canonical tracial class functions related to its irreducible representations. Such functions form an orthogonal basis for the class functions, extending ideas from Fourier analysis to this domain, and accord a very explicit structure. Exploiting a tensorial structure on representations, which translates into a multiplicative algebra structure for  irreducible characters,  we propose to efficiently approximate class functions using polynomials in a small number of such characters. Thus, our approach provides a global, non-linear coordinate system to describe functions on the group that is intrinsic in nature, in the sense that it is independent of local charts, and can be easily computed in concrete models.  We demonstrate that such non-linear approximation using a small dictionary can be effectively implemented using a deep neural network paradigm. This allows us to learn a class function efficiently from a dataset of its outputs.
Few-shot graph link prediction with domain adaptation
Authors: Hao Zhu, Mahashweta Das, Mangesh Bendre, Fei Wang, Hao Yang, Soha Hassoun
Date: 2021-09-29 00:00:00
Tasks: Domain Adaptation, Few-Shot Learning, Link Prediction, Prediction

Real world link prediction problem often deals with data coming from multiple imbalanced domains. Similar problems in computer vision are often referred to as Few-Shot Learning (FSL) problems. However, for graph link prediction, this problem has rarely been addressed and explored. In this work, we propose an adversarial training based modification to the current state-of-the-arts link prediction method to solve this problem. We introduce a domain discriminator on pairs of graph-level embedding. We then use the discriminator to improve the model in an adversarial way, such that the graph embeddings generated by the model are domain agnostic. We test our proposal on 3 benchmark datasets. Our results demonstrate that, when domain differences exist, our method creates better graph embeddings that are more evenly distributed across domains and generates better prediction outcomes.
Efficient Reinforcement Learning Experimentation in PyTorch
Authors: Albert Bou, Sebastian Dittert, Gianni de Fabritiis
Date: 2021-09-29 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Abstract: Deep reinforcement learning (RL) has proved successful at solving challenging environments but often requires long training times and very many samples. Furthermore, advancing artificial intelligence requires to easily prototype new methods, yet avoiding impractically slow experimental turnaround times. To this end, we present a PyTorch-based library for RL with a modular design that allows composing agents based on three components types: actors, storages and algorithms. Additionally, the definition of synchronous and asynchronous architectures is permitted with flexibility and independence of the components. We present several standard use-cases of the library and showcase its potential by obtaining the highest to-date test performance on the Obstacle Tower Unity3D challenge environment. In summary, we believe that this work helps accelerate experimentation of new ideas, simplifying research and enabling to tackle more challenging RL problems.
Co-variance: Tackling Noisy Labels with Sample Selection by Emphasizing High-variance Examples
Authors: Xiaobo Xia, Bo Han, Yibing Zhan, Jun Yu, Mingming Gong, Chen Gong, Tongliang Liu
Date: 2021-09-29 00:00:00
Tasks: Learning with noisy labels

The sample selection approach is popular in learning with noisy labels, which tends to select potentially clean data out of noisy data for robust training. The state-of-the-art methods train two deep networks simultaneously for sample selection, which aims to employ their different learning abilities. To prevent two networks from converging to a consensus, their divergence should be maintained during training. Typically, the divergence is kept by ﬁrst locating the disagreement data on which the prediction labels of two networks are different, and then selecting clean data out of such data. However, this procedure is sample-inefﬁcient for network weight updates, which means that a few clean examples can be utilized in training. In this paper, to address the issues, we propose a simple yet effective method called Co-variance. In particular, we select possibly clean data that simultaneously have high-variance prediction probabilities between two networks. As selected data have high variances, the divergence of two networks can be maintained by training on such data. Additionally, the condition of high variances is milder than the condition of disagreement in sample selection, which allows more data to be considered for training, and makes our method more sample-efﬁcient. Moreover, we show that the proposed method enables to mine enough hard clean examples to help generalization. A series of empirical results show that Co-variance is superior to multiple baselines in the robustness of trained models, especially on class-imbalanced and real-world noisy datasets.
Critical Points in Quantum Generative Models
Authors: Eric Ricardo Anschuetz
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4

One of the most important properties of neural networks is the clustering of local minima of the loss function near the global minimum, enabling efficient training. Though generative models implemented on quantum computers are known to be more expressive than their traditional counterparts, it has empirically been observed that these models experience a transition in the quality of their local minima. Namely, below some critical number of parameters, all local minima are far from the global minimum in function value; above this critical parameter count, all local minima are good approximators of the global minimum. Furthermore, for a certain class of quantum generative models, this transition has empirically been observed to occur at parameter counts exponentially large in the problem size, meaning practical training of these models is out of reach. Here, we give the first proof of this transition in trainability, specializing to this latter class of quantum generative model. We use techniques inspired by those used to study the loss landscapes of classical neural networks. We also verify that our analytic results hold experimentally even at modest model sizes.
Learning the Representation of Behavior Styles with Imitation Learning
Authors: Xiao Liu, Meng Wang, Zhaorong Wang, Yingfeng Chen, Yujing Hu, Changjie Fan, Chongjie Zhang
Date: 2021-09-29 00:00:00
Tasks: Imitation Learning

Imitation learning is one of the methods for reproducing expert demonstrations adaptively by learning a mapping between observations and actions. However, behavior styles such as motion trajectory and driving habit depend largely on the dataset of human maneuvers, and settle down to an average behavior style in most imitation learning algorithms. In this study, we propose a method named style behavior cloning (Style BC), which can not only infer the latent representation of behavior styles automatically, but also imitate different style policies from expert demonstrations. Our method is inspired by the word2vec algorithm and we construct a behavior-style to action mapping which is similar to the word-embedding to context mapping in word2vec. Empirical results on popular benchmark environments show that Style BC outperforms standard behavior cloning in prediction accuracy and expected reward significantly. Furthermore, compared with various baselines, our policy influenced by its assigned style embedding can better reproduce the expert behavior styles, especially in the complex environments or the number of the behavior styles is large.
Open Set Domain Adaptation with Zero-shot Learning on Graph
Authors: Xinyue Zhang, Xu Yang, Zhi-Yong Liu
Date: 2021-09-29 00:00:00
Tasks: Domain Adaptation, Zero-Shot Learning

Open set domain adaptation focuses on transferring the information from a richly labeled domain called \emph{source domain} to a scarcely labeled domain called \emph{target domain} while classifying the unseen target samples as one \emph{unknown} class in an unsupervised way. Compared with the close set domain adaptation, where the source domain and the target domain share the same class space, the classification of the unknown class makes it easy to adapt to the realistic environment. Particularly, after the recognition of the unknown samples, the robot can either ask for manually labeling or further develop the classification ability of the unknown classes based on pre-stored knowledge. Inspired by this idea, in this paper we propose a model for open set domain adaptation with zero-shot learning on the unknown classes. We utilize adversarial learning to align the two domains while rejecting the unknown classes. Then the knowledge graph is introduced to generate the classifiers for the unknown classes with the employment of the graph convolution network (GCN). Thus the classification ability of the source domain is transferred to the target domain and the model can distinguish the unknown classes with prior knowledge. We evaluate our model on digits datasets and the result shows superior performance.
ACCTS: an Adaptive Model Training Policy for Continuous Classification of Time Series
Authors: Chenxi Sun, Moxian Song, Derun Cai, Shenda Hong, Hongyan Li
Date: 2021-09-29 00:00:00
Tasks: Continual Learning, Time Series, Time Series Analysis

More and more real-world applications require to classify time series at every time. For example, critical patients should be detected for vital signs and diagnosed at all times to facilitate timely life-saving. For this demand, we propose a new concept, Continuous Classification of Time Series (CCTS), to achieve the high-accuracy classification at every time. Time series always evolves dynamically, changing features introducing the multi-distribution form. Thus, different from the existing one-shot classification, the key of CCTS is to model multiple distributions simultaneously. However, most models are hard to achieve it due to their independent identically distributed premise. If a model learns a new distribution, it will likely forget old ones. And if a model repeatedly learns similar data, it will likely be overfitted. Thus, two main problems are the catastrophic forgetting and the over fitting. In this work, we define CCTS as a continual learning task with the unclear distribution division. But different divisions differently affect two problems and a fixed division rule may become invalid as time series evolves. In order to overcome two main problems and finally achieve CCTS, we propose a novel Adaptive model training policy - ACCTS. Its adaptability represents in two aspects: (1) Adaptive multi-distribution extraction policy. Instead of the fixed rules and the prior knowledge, ACCTS extracts data distributions adaptive to the time series evolution and the model change; (2) Adaptive importance-based replay policy. Instead of reviewing all old distributions, ACCTS only replays the important samples adaptive to the contribution of data to the model. Experiments on four real-world datasets show that our method can classify more accurately than all baselines at every time.
Polyphonic Music Composition: An Adversarial Inverse Reinforcement Learning Approach
Authors: Kelvin Xavier Munguia Velez, Von-Wun Soo
Date: 2021-09-29 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Most  recent  approaches  to  automatic  music  harmony composition adopt deep supervised learning to  train  a  model  using  a  set  of  human  composed songs as training data.  However, these approaches suffer  from  inherent  limitations  from  the  chosen deep learning models which may lead to unpleasing harmonies.  This paper explores an alternative approach to harmony composition using a combination  of  novel  Deep  Supervised  Learning,  DeepReinforcement  Learning  and  Inverse  Reinforcement Learning techniques.  In this novel approach, our  model  selects  the  next  chord  in  the  composition(action)  based  on  the  previous  notes(states), therefore allowing us to model harmony composition as a reinforcement learning problem in which we  look  to  maximize  an  overall  accumulated  reward.   However,  designing  an  appropriate reward function is known to be a very tricky and difficult process.  To  overcome  this  problem  we  propose learning  a  reward  function  from  a  set  of  human-composed  tracks  using  Adversarial  Inverse  Reinforcement Learning. We start by training a Bi-axial LSTM  model  using  supervised  learning  and  improve upon it by tuning it using Deep Q-learning. Instead of using GANs to generate a similar music composition  to  human  compositions  directly,  we adopt GANs to learn the reward function of the music trajectories from human compositions. We then combine the learned reward function with a reward based on music theory rules to improve the generation of the model trained by supervised learning. The  results  show  improvement  over  a  pre-trained model  without  training  with  reinforcement  learning with respect to a set of objective metrics and preference from subjective user evaluation.
Kalman Filter Is All You Need: Optimization Works When Noise Estimation Fails
Authors: Ido Greenberg, Shie Mannor, Netanel Yannay
Date: 2021-09-29 00:00:00
Tasks: All, Noise Estimation, State Estimation

Determining the noise parameters of a Kalman Filter (KF) has been studied for decades. A huge body of research focuses on the task of noise estimation under various conditions, since precise noise estimation is considered equivalent to minimization of the filtering errors. However, we show that even a small violation of the KF assumptions can significantly modify the effective noise, breaking the equivalence between the tasks and making noise estimation an inferior strategy. We show that such violations are common, and are often not trivial to handle or even notice. Consequentially, we argue that a robust solution is needed - rather than choosing a dedicated model per problem.
To that end, we apply gradient-based optimization to the filtering errors directly, with relation to an efficient parameterization of the symmetric and positive-definite parameters of the KF. In a variety of state-estimation and tracking problems, we show that the optimization improves both the accuracy of the KF and its robustness to design decisions.
In addition, we demonstrate how an optimized neural network model can seem to reduce the errors significantly compared to a KF - and how this reduction vanishes once the KF is optimized similarly. This indicates how complicated models can be wrongly identified as superior to the KF, while in fact they were merely more optimized.
Federated Learning with Data-Agnostic Distribution Fusion
Authors: Jian-hui Duan, Wenzhong Li, Sanglu Lu
Date: 2021-09-29 00:00:00
Proceeding: CVPR 2023 1
Tasks: Federated Learning

    Federated learning has emerged as a promising distributed machine learning paradigm to preserve data privacy. One of the fundamental challenges of federated learning is that data samples across clients are usually not independent and identically distributed (non-IID), leading to slow convergence and severe performance drop of the aggregated global model. In this paper, we propose a novel data-agnostic distribution fusion based model aggregation method called \texttt{FedDAF} to optimize federated learning with non-IID local datasets, based on which the heterogeneous clients' data distributions can be represented by the fusion of several virtual components with different parameters and weights. We develop a variational autoencoder (VAE) method to derive the optimal parameters for the fusion distribution using the limited statistical information extracted from local models, which optimizes model aggregation for federated learning by solving a probabilistic maximization problem. Extensive experiments based on various federated learning scenarios with real-world datasets show that \texttt{FedDAF} achieves significant performance improvement compared to the state-of-the-art.
Defending Against Backdoor Attacks Using Ensembles of Weak Learners
Authors: Charles Jin, Melinda Sun, Martin Rinard
Date: 2021-09-29 00:00:00
Tasks: Backdoor Attack, Data Poisoning

A recent line of work has shown that deep networks are susceptible to backdoor data poisoning attacks. Specifically, by injecting a small amount of malicious data into the training distribution, an adversary gains the ability to control the behavior of the model during inference. We propose an iterative training procedure for removing poisoned data from the training set. Our approach consists of two steps. We first train an ensemble of weak learners to automatically discover distinct subpopulations in the training set. We then leverage a boosting framework to exclude the poisoned data and recover the clean data. Our algorithm is based on a novel bootstrapped measure of generalization, which provably separates the clean from the dirty data under mild assumptions. Empirically, our method successfully defends against a state-of-the-art dirty label backdoor attack. We find that our approach significantly outperforms previous defenses.