pwc-archive/papers-with-abstracts huggingface dataset:
Persformer: A Transformer Architecture for Topological Machine Learning
Authors: Raphael Reinauer, Matteo Caorsi, Nicolas Berkouk
Date: 2021-12-30 00:00:00
Tasks: BIG-bench Machine Learning, Topological Data Analysis

One of the main challenges of Topological Data Analysis (TDA) is to extract features from persistent diagrams directly usable by machine learning algorithms. Indeed, persistence diagrams are intrinsically (multi-)sets of points in $\mathbb{R}^2$ and cannot be seen in a straightforward manner as vectors. In this article, we introduce $\texttt{Persformer}$, the first Transformer neural network architecture that accepts persistence diagrams as input. The $\texttt{Persformer}$ architecture significantly outperforms previous topological neural network architectures on classical synthetic and graph benchmark datasets. Moreover, it satisfies a universal approximation theorem. This allows us to introduce the first interpretability method for topological machine learning, which we explore in two examples.
Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling
Authors: Dmitry Kovalev, Alexander Gasnikov, Peter Richtárik
Date: 2021-12-30 00:00:00

In this paper we study the convex-concave saddle-point problem $\min_x \max_y f(x) + y^T \mathbf{A} x - g(y)$, where $f(x)$ and $g(y)$ are smooth and convex functions. We propose an Accelerated Primal-Dual Gradient Method (APDG) for solving this problem, achieving (i) an optimal linear convergence rate in the strongly-convex-strongly-concave regime, matching the lower complexity bound (Zhang et al., 2021), and (ii) an accelerated linear convergence rate in the case when only one of the functions $f(x)$ and $g(y)$ is strongly convex or even none of them are. Finally, we obtain a linearly convergent algorithm for the general smooth and convex-concave saddle point problem $\min_x \max_y F(x,y)$ without the requirement of strong convexity or strong concavity.
Improving Learning-to-Defer Algorithms Through Fine-Tuning
Authors: Naveen Raman, Michael Yee
Date: 2021-12-18 00:00:00

The ubiquity of AI leads to situations where humans and AI work together, creating the need for learning-to-defer algorithms that determine how to partition tasks between AI and humans. We work to improve learning-to-defer algorithms when paired with specific individuals by incorporating two fine-tuning algorithms and testing their efficacy using both synthetic and image datasets. We find that fine-tuning can pick up on simple human skill patterns, but struggles with nuance, and we suggest future work that uses robust semi-supervised to improve learning.
Multi-Agent Reinforcement Learning via Adaptive Kalman Temporal Difference and Successor Representation
Authors: Mohammad Salimibeni, Arash Mohammadi, Parvin Malekzadeh, Konstantinos N. Plataniotis
Date: 2021-12-30 00:00:00
Tasks: Multi-agent Reinforcement Learning, OpenAI Gym, reinforcement-learning, Reinforcement Learning (RL)

Distributed Multi-Agent Reinforcement Learning (MARL) algorithms has attracted a surge of interest lately mainly due to the recent advancements of Deep Neural Networks (DNNs). Conventional Model-Based (MB) or Model-Free (MF) RL algorithms are not directly applicable to the MARL problems due to utilization of a fixed reward model for learning the underlying value function. While DNN-based solutions perform utterly well when a single agent is involved, such methods fail to fully generalize to the complexities of MARL problems. In other words, although recently developed approaches based on DNNs for multi-agent environments have achieved superior performance, they are still prone to overfiting, high sensitivity to parameter selection, and sample inefficiency. The paper proposes the Multi-Agent Adaptive Kalman Temporal Difference (MAK-TD) framework and its Successor Representation-based variant, referred to as the MAK-SR. Intuitively speaking, the main objective is to capitalize on unique characteristics of Kalman Filtering (KF) such as uncertainty modeling and online second order learning. The proposed MAK-TD/SR frameworks consider the continuous nature of the action-space that is associated with high dimensional multi-agent environments and exploit Kalman Temporal Difference (KTD) to address the parameter uncertainty. By leveraging the KTD framework, SR learning procedure is modeled into a filtering problem, where Radial Basis Function (RBF) estimators are used to encode the continuous space into feature vectors. On the other hand, for learning localized reward functions, we resort to Multiple Model Adaptive Estimation (MMAE), to deal with the lack of prior knowledge on the observation noise covariance and observation mapping function. The proposed MAK-TD/SR frameworks are evaluated via several experiments, which are implemented through the OpenAI Gym MARL benchmarks.
Soundify: Matching Sound Effects to Video
Authors: David Chuan-En Lin, Anastasis Germanidis, Cristóbal Valenzuela, Yining Shi, Nikolas Martelaro
Date: 2021-12-17 00:00:00
Tasks: Audio Generation, Image Classification, Video Editing, Zero-Shot Image Classification

In the art of video editing, sound helps add character to an object and immerse the viewer within a space. Through formative interviews with professional editors (N=10), we found that the task of adding sounds to video can be challenging. This paper presents Soundify, a system that assists editors in matching sounds to video. Given a video, Soundify identifies matching sounds, synchronizes the sounds to the video, and dynamically adjusts panning and volume to create spatial audio. In a human evaluation study (N=889), we show that Soundify is capable of matching sounds to video out-of-the-box for a diverse range of audio categories. In a within-subjects expert study (N=12), we demonstrate the usefulness of Soundify in helping video editors match sounds to video with lighter workload, reduced task completion time, and improved usability.
Accelerated Proximal Alternating Gradient-Descent-Ascent for Nonconvex Minimax Machine Learning
Authors: Ziyi Chen, Shaocong Ma, Yi Zhou
Date: 2021-12-22 00:00:00
Tasks: BIG-bench Machine Learning

Alternating gradient-descent-ascent (AltGDA) is an optimization algorithm that has been widely used for model training in various machine learning applications, which aims to solve a nonconvex minimax optimization problem. However, the existing studies show that it suffers from a high computation complexity in nonconvex minimax optimization. In this paper, we develop a single-loop and fast AltGDA-type algorithm that leverages proximal gradient updates and momentum acceleration to solve regularized nonconvex minimax optimization problems. By leveraging the momentum acceleration technique, we prove that the algorithm converges to a critical point in nonconvex minimax optimization and achieves a computation complexity in the order of $\mathcal{O}(\kappa^{\frac{11}{6}}\epsilon^{-2})$, where $\epsilon$ is the desired level of accuracy and $\kappa$ is the problem's condition number. {Such a computation complexity improves the state-of-the-art complexities of single-loop GDA and AltGDA algorithms (see the summary of comparison in \Cref{table1})}. We demonstrate the effectiveness of our algorithm via an experiment on adversarial deep learning.
Resource-Efficient Deep Learning: A Survey on Model-, Arithmetic-, and Implementation-Level Techniques
Authors: JunKyu Lee, Lev Mukhanov, Amir Sabbagh Molahosseini, Umar Minhas, Yang Hua, Jesus Martinez del Rincon, Kiril Dichev, Cheol-Ho Hong, Hans Vandierendonck
Date: 2021-12-30 00:00:00
Tasks: Deep Learning, Face Recognition, Self-Driving Cars, Survey

Deep learning is pervasive in our daily life, including self-driving cars, virtual assistants, social network services, healthcare services, face recognition, etc. However, deep neural networks demand substantial compute resources during training and inference. The machine learning community has mainly focused on model-level optimizations such as architectural compression of deep learning models, while the system community has focused on implementation-level optimization. In between, various arithmetic-level optimization techniques have been proposed in the arithmetic community. This article provides a survey on resource-efficient deep learning techniques in terms of model-, arithmetic-, and implementation-level techniques and identifies the research gaps for resource-efficient deep learning techniques across the three different level techniques. Our survey clarifies the influence from higher to lower-level techniques based on our resource-efficiency metric definition and discusses the future trend for resource-efficient deep learning research.
Explanation as Question Answering based on Design Knowledge
Authors: Ashok Goel, Vrinda Nandan, Eric Gregori, Sungeun An, Spencer Rugaber
Date: 2021-12-16 00:00:00
Tasks: AI Agent, Open-Ended Question Answering, Question Answering

Explanation of an AI agent requires knowledge of its design and operation. An open question is how to identify, access and use this design knowledge for generating explanations. Many AI agents used in practice, such as intelligent tutoring systems fielded in educational contexts, typically come with a User Guide that explains what the agent does, how it works and how to use the agent. However, few humans actually read the User Guide in detail. Instead, most users seek answers to their questions on demand. In this paper, we describe a question answering agent (AskJill) that uses the User Guide for an interactive learning environment (VERA) to automatically answer questions and thereby explains the domain, functioning, and operation of VERA. We present a preliminary assessment of AskJill in VERA.
Confidence-Aware Subject-to-Subject Transfer Learning for Brain-Computer Interface
Authors: Dong-Kyun Han, Serkan Musellim, Dong-Young Kim, Ji-Hoon Jeong
Date: 2021-12-15 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), Subject Transfer, Transfer Learning

The inter/intra-subject variability of electroencephalography (EEG) makes the practical use of the brain-computer interface (BCI) difficult. In general, the BCI system requires a calibration procedure to tune the model every time the system is used. This problem is recognized as a major obstacle to BCI, and to overcome it, approaches based on transfer learning (TL) have recently emerged. However, many BCI paradigms are limited in that they consist of a structure that shows labels first and then measures "imagery", the negative effects of source subjects containing data that do not contain control signals have been ignored in many cases of the subject-to-subject TL process. The main purpose of this paper is to propose a method of excluding subjects that are expected to have a negative impact on subject-to-subject TL training, which generally uses data from as many subjects as possible. In this paper, we proposed a BCI framework using only high-confidence subjects for TL training. In our framework, a deep neural network selects useful subjects for the TL process and excludes noisy subjects, using a co-teaching algorithm based on the small-loss trick. We experimented with leave-one-subject-out validation on two public datasets (2020 international BCI competition track 4 and OpenBMI dataset). Our experimental results showed that confidence-aware TL, which selects subjects with small loss instances, improves the generalization performance of BCI.
On the Role of Neural Collapse in Transfer Learning
Authors: Tomer Galanti, András György, Marcus Hutter
Date: 2021-12-30 00:00:00
Proceeding: ICLR 2022 4
Tasks: Clustering, Few-Shot Learning, Transfer Learning

We study the ability of foundation models to learn representations for classification that are transferable to new, unseen classes. Recent results in the literature show that representations learned by a single classifier over many classes are competitive on few-shot learning problems with representations learned by special-purpose algorithms designed for such problems. In this paper we provide an explanation for this behavior based on the recently observed phenomenon that the features learned by overparameterized classification networks show an interesting clustering property, called neural collapse. We demonstrate both theoretically and empirically that neural collapse generalizes to new samples from the training classes, and -- more importantly -- to new classes as well, allowing foundation models to provide feature maps that work well in transfer learning and, specifically, in the few-shot setting.
Audio-to-symbolic Arrangement via Cross-modal Music Representation Learning
Authors: Ziyu Wang, Dejing Xu, Gus Xia, Ying Shan
Date: 2021-12-30 00:00:00
Tasks: Representation Learning

Could we automatically derive the score of a piano accompaniment based on the audio of a pop song? This is the audio-to-symbolic arrangement problem we tackle in this paper. A good arrangement model should not only consider the audio content but also have prior knowledge of piano composition (so that the generation "sounds like" the audio and meanwhile maintains musicality). To this end, we contribute a cross-modal representation-learning model, which 1) extracts chord and melodic information from the audio, and 2) learns texture representation from both audio and a corrupted ground truth arrangement. We further introduce a tailored training strategy that gradually shifts the source of texture information from corrupted score to audio. In the end, the score-based texture posterior is reduced to a standard normal distribution, and only audio is needed for inference. Experiments show that our model captures major audio information and outperforms baselines in generation quality.
Overview of the EEG Pilot Subtask at MediaEval 2021: Predicting Media Memorability
Authors: Lorin Sweeney, Ana Matran-Fernandez, Sebastian Halder, Alba G. Seco de Herrera, Alan Smeaton, Graham Healy
Date: 2021-12-15 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

The aim of the Memorability-EEG pilot subtask at MediaEval'2021 is to promote interest in the use of neural signals -- either alone or in combination with other data sources -- in the context of predicting video memorability by highlighting the utility of EEG data. The dataset created consists of pre-extracted features from EEG recordings of subjects while watching a subset of videos from Predicting Media Memorability subtask 1. This demonstration pilot gives interested researchers a sense of how neural signals can be used without any prior domain knowledge, and enables them to do so in a future memorability task. The dataset can be used to support the exploration of novel machine learning and processing strategies for predicting video memorability, while potentially increasing interdisciplinary interest in the subject of memorability, and opening the door to new combined EEG-computer vision approaches.
Empirical Evaluation of Deep Learning Models for Knowledge Tracing: Of Hyperparameters and Metrics on Performance and Replicability
Authors: Sami Sarsa, Juho Leinonen, Arto Hellas
Date: 2021-12-30 00:00:00
Tasks: Knowledge Tracing

We review and evaluate a body of deep learning knowledge tracing (DLKT) models with openly available and widely-used data sets, and with a novel data set of students learning to program. The evaluated knowledge tracing models include Vanilla-DKT, two Long Short-Term Memory Deep Knowledge Tracing (LSTM-DKT) variants, two Dynamic Key-Value Memory Network (DKVMN) variants, and Self-Attentive Knowledge Tracing (SAKT). As baselines, we evaluate simple non-learning models, logistic regression and Bayesian Knowledge Tracing (BKT). To evaluate how different aspects of DLKT models influence model performance, we test input and output layer variations found in the compared models that are independent of the main architectures. We study maximum attempt count options, including filtering out long attempt sequences, that have been implicitly and explicitly used in prior studies. We contrast the observed performance variations against variations from non-model properties such as randomness and hardware. Performance of models is assessed using multiple metrics, whereby we also contrast the impact of the choice of metric on model performance. The key contributions of this work are: Evidence that DLKT models generally outperform more traditional models, but not necessarily by much and not always; Evidence that even simple baselines with little to no predictive value may outperform DLKT models, especially in terms of accuracy -- highlighting importance of selecting proper baselines for comparison; Disambiguation of properties that affect performance in DLKT models including metric choice, input and output layer variations, common hyperparameters, random seeding and hardware; Discussion of issues in replicability when evaluating DLKT models, including discrepancies in prior reported results and methodology. Model implementations, evaluation code, and data are published as a part of this work.
EDAssistant: Supporting Exploratory Data Analysis in Computational Notebooks with In-Situ Code Search and Recommendation
Authors: Xingjun Li, Yizhi Zhang, Justin Leung, Chengnian Sun, Jian Zhao
Date: 2021-12-15 00:00:00
Tasks: Code Search

Using computational notebooks (e.g., Jupyter Notebook), data scientists rationalize their exploratory data analysis (EDA) based on their prior experience and external knowledge such as online examples. For novices or data scientists who lack specific knowledge about the dataset or problem to investigate, effectively obtaining and understanding the external information is critical to carry out EDA. This paper presents EDAssistant, a JupyterLab extension that supports EDA with in-situ search of example notebooks and recommendation of useful APIs, powered by novel interactive visualization of search results. The code search and recommendation are enabled by state-of-the-art machine learning models, trained on a large corpus of EDA notebooks collected online. A user study is conducted to investigate both EDAssistant and data scientists' current practice (i.e., using external search engines). The results demonstrate the effectiveness and usefulness of EDAssistant, and participants appreciated its smooth and in-context support of EDA. We also report several design implications regarding code recommendation tools.
SAE: Sequential Anchored Ensembles
Authors: Arnaud Delaunoy, Gilles Louppe
Date: 2021-12-30 00:00:00

Computing the Bayesian posterior of a neural network is a challenging task due to the high-dimensionality of the parameter space. Anchored ensembles approximate the posterior by training an ensemble of neural networks on anchored losses designed for the optima to follow the Bayesian posterior. Training an ensemble, however, becomes computationally expensive as its number of members grows since the full training procedure is repeated for each member. In this note, we present Sequential Anchored Ensembles (SAE), a lightweight alternative to anchored ensembles. Instead of training each member of the ensemble from scratch, the members are trained sequentially on losses sampled with high auto-correlation, hence enabling fast convergence of the neural networks and efficient approximation of the Bayesian posterior. SAE outperform anchored ensembles, for a given computational budget, on some benchmarks while showing comparable performance on the others and achieved 2nd and 3rd place in the light and extended tracks of the NeurIPS 2021 Approximate Inference in Bayesian Deep Learning competition.
Dimensionality reduction for prediction: Application to Bitcoin and Ethereum
Authors: Hugo Inzirillo, Benjamin Mat
Date: 2021-12-30 00:00:00
Tasks: Dimensionality Reduction, Prediction

The objective of this paper is to assess the performances of dimensionality reduction techniques to establish a link between cryptocurrencies. We have focused our analysis on the two most traded cryptocurrencies: Bitcoin and Ethereum. To perform our analysis, we took log returns and added some covariates to build our data set. We first introduced the pearson correlation coefficient in order to have a preliminary assessment of the link between Bitcoin and Ethereum. We then reduced the dimension of our data set using canonical correlation analysis and principal component analysis. After performing an analysis of the links between Bitcoin and Ethereum with both statistical techniques, we measured their performance on forecasting Ethereum returns with Bitcoin s features.
Constructing a Good Behavior Basis for Transfer using Generalized Policy Updates
Authors: Safa Alver, Doina Precup
Date: 2021-12-30 00:00:00
Proceeding: ICLR 2022 4
Tasks: reinforcement-learning, Reinforcement Learning (RL)

We study the problem of learning a good set of policies, so that when combined together, they can solve a wide variety of unseen reinforcement learning tasks with no or very little new data. Specifically, we consider the framework of generalized policy evaluation and improvement, in which the rewards for all tasks of interest are assumed to be expressible as a linear combination of a fixed set of features. We show theoretically that, under certain assumptions, having access to a specific set of diverse policies, which we call a set of independent policies, can allow for instantaneously achieving high-level performance on all possible downstream tasks which are typically more complex than the ones on which the agent was trained. Based on this theoretical analysis, we propose a simple algorithm that iteratively constructs this set of policies. In addition to empirically validating our theoretical results, we compare our approach with recently proposed diverse policy set construction methods and show that, while others fail, our approach is able to build a behavior basis that enables instantaneous transfer to all possible downstream tasks. We also show empirically that having access to a set of independent policies can better bootstrap the learning process on downstream tasks where the new reward function cannot be described as a linear combination of the features. Finally, we demonstrate how this policy set can be useful in a lifelong reinforcement learning setting.