pwc-archive/papers-with-abstracts huggingface dataset:
Universal Adversarial Attacks on Neural Networks for Power Allocation in a Massive MIMO System
Authors: Pablo Millán Santos, B. R. Manoj, Meysam Sadeghi, Erik G. Larsson
Date: 2021-10-10 00:00:00
Tasks: regression

Deep learning (DL) architectures have been successfully used in many applications including wireless systems. However, they have been shown to be susceptible to adversarial attacks. We analyze DL-based models for a regression problem in the context of downlink power allocation in massive multiple-input-multiple-output systems and propose universal adversarial perturbation (UAP)-crafting methods as white-box and black-box attacks. We benchmark the UAP performance of white-box and black-box attacks for the considered application and show that the adversarial success rate can achieve up to 60% and 40%, respectively. The proposed UAP-based attacks make a more practical and realistic approach as compared to classical white-box attacks.
Widen The Backdoor To Let More Attackers In
Authors: Siddhartha Datta, Giulio Lovisotto, Ivan Martinovic, Nigel Shadbolt
Date: 2021-10-09 00:00:00
Tasks: Backdoor Attack

As collaborative learning and the outsourcing of data collection become more common, malicious actors (or agents) which attempt to manipulate the learning process face an additional obstacle as they compete with each other. In backdoor attacks, where an adversary attempts to poison a model by introducing malicious samples into the training data, adversaries have to consider that the presence of additional backdoor attackers may hamper the success of their own backdoor. In this paper, we investigate the scenario of a multi-agent backdoor attack, where multiple non-colluding attackers craft and insert triggered samples in a shared dataset which is used by a model (a defender) to learn a task. We discover a clear backfiring phenomenon: increasing the number of attackers shrinks each attacker's attack success rate (ASR). We then exploit this phenomenon to minimize the collective ASR of attackers and maximize defender's robustness accuracy by (i) artificially augmenting the number of attackers, and (ii) indexing to remove the attacker's sub-dataset from the model for inference, hence proposing 2 defenses.
Aura: Privacy-preserving Augmentation to Improve Test Set Diversity in Speech Enhancement
Authors: Xavier Gitiaux, Aditya Khant, Ebrahim Beyrami, Chandan Reddy, Jayant Gupchup, Ross Cutler
Date: 2021-10-08 00:00:00
Tasks: Diversity, Privacy Preserving, Speech Enhancement

Noise suppression models running in production environments are commonly trained on publicly available datasets. However, this approach leads to regressions due to the lack of training/testing on representative customer data. Moreover, due to privacy reasons, developers cannot listen to customer content. This `ears-off' situation motivates augmenting existing datasets in a privacy-preserving manner. In this paper, we present Aura, a solution to make existing noise suppression test sets more challenging and diverse while being sample efficient. Aura is `ears-off' because it relies on a feature extractor and a metric of speech quality, DNSMOS P.835, both pre-trained on data obtained from public sources. As an application of Aura, we augment the INTERSPEECH 2021 DNS challenge by sampling audio files from a new batch of data of 20K clean speech clips from Librivox mixed with noise clips obtained from AudioSet. Aura makes the existing benchmark test set harder by 0.27 in DNSMOS P.835 OVLR (7%), 0.64 harder in DNSMOS P.835 SIG (16%), increases diversity by 31%, and achieves a 26% improvement in Spearman's rank correlation coefficient (SRCC) compared to random sampling. Finally, we open-source Aura to stimulate research of test set development.
Designing Composites with Target Effective Young's Modulus using Reinforcement Learning
Authors: Aldair E. Gongora, Siddharth Mysore, Beichen Li, Wan Shou, Wojciech Matusik, Elise F. Morgan, Keith A. Brown, Emily Whiting
Date: 2021-10-07 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Advancements in additive manufacturing have enabled design and fabrication of materials and structures not previously realizable. In particular, the design space of composite materials and structures has vastly expanded, and the resulting size and complexity has challenged traditional design methodologies, such as brute force exploration and one factor at a time (OFAT) exploration, to find optimum or tailored designs. To address this challenge, supervised machine learning approaches have emerged to model the design space using curated training data; however, the selection of the training data is often determined by the user. In this work, we develop and utilize a Reinforcement learning (RL)-based framework for the design of composite structures which avoids the need for user-selected training data. For a 5 $\times$ 5 composite design space comprised of soft and compliant blocks of constituent material, we find that using this approach, the model can be trained using 2.78% of the total design space consists of $2^{25}$ design possibilities. Additionally, the developed RL-based framework is capable of finding designs at a success rate exceeding 90%. The success of this approach motivates future learning frameworks to utilize RL for the design of composites and other material systems.
Satisficing Paths and Independent Multi-Agent Reinforcement Learning in Stochastic Games
Authors: Bora Yongacoglu, Gürdal Arslan, Serdar Yüksel
Date: 2021-10-09 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

In multi-agent reinforcement learning (MARL), independent learners are those that do not observe the actions of other agents in the system. Due to the decentralization of information, it is challenging to design independent learners that drive play to equilibrium. This paper investigates the feasibility of using satisficing dynamics to guide independent learners to approximate equilibrium in stochastic games. For $\epsilon \geq 0$, an $\epsilon$-satisficing policy update rule is any rule that instructs the agent to not change its policy when it is $\epsilon$-best-responding to the policies of the remaining players; $\epsilon$-satisficing paths are defined to be sequences of joint policies obtained when each agent uses some $\epsilon$-satisficing policy update rule to select its next policy. We establish structural results on the existence of $\epsilon$-satisficing paths into $\epsilon$-equilibrium in both symmetric $N$-player games and general stochastic games with two players. We then present an independent learning algorithm for $N$-player symmetric games and give high probability guarantees of convergence to $\epsilon$-equilibrium under self-play. This guarantee is made using symmetry alone, leveraging the previously unexploited structure of $\epsilon$-satisficing paths.
Convex-Concave Min-Max Stackelberg Games
Authors: Denizalp Goktas, Amy Greenwald
Date: 2021-10-05 00:00:00
Proceeding: NeurIPS 2021 12

Min-max optimization problems (i.e., min-max games) have been attracting a great deal of attention because of their applicability to a wide range of machine learning problems. Although significant progress has been made recently, the literature to date has focused on games with independent strategy sets; little is known about solving games with dependent strategy sets, which can be characterized as min-max Stackelberg games. We introduce two first-order methods that solve a large class of convex-concave min-max Stackelberg games, and show that our methods converge in polynomial time. Min-max Stackelberg games were first studied by Wald, under the posthumous name of Wald's maximin model, a variant of which is the main paradigm used in robust optimization, which means that our methods can likewise solve many convex robust optimization problems. We observe that the computation of competitive equilibria in Fisher markets also comprises a min-max Stackelberg game. Further, we demonstrate the efficacy and efficiency of our algorithms in practice by computing competitive equilibria in Fisher markets with varying utility structures. Our experiments suggest potential ways to extend our theoretical results, by demonstrating how different smoothness properties can affect the convergence rate of our algorithms.
Topological Data Analysis (TDA) Techniques Enhance Hand Pose Classification from ECoG Neural Recordings
Authors: Simone Azeglio, Arianna Di Bernardo, Gabriele Penna, Fabrizio Pittatore, Simone Poetto, Johannes Gruenwald, Christoph Kapeller, Kyousuke Kamada, Christoph Guger
Date: 2021-10-09 00:00:00
Tasks: Brain Computer Interface, Feature Importance, Hyperparameter Optimization, Model Selection, Time Series, Time Series Analysis, Topological Data Analysis

Electrocorticogram (ECoG) well characterizes hand movement intentions and gestures. In the present work we aim to investigate the possibility to enhance hand pose classification, in a Rock-Paper-Scissor - and Rest - task, by introducing topological descriptors of time series data. We hypothesized that an innovative approach based on topological data analysis can extract hidden information that are not detectable with standard Brain Computer Interface (BCI)techniques. To investigate this hypothesis, we integrate topological features together with power band features and feed them to several standard classifiers, e.g. Random Forest,Gradient Boosting. Model selection is thus completed after a meticulous phase of bayesian hyperparameter optimization. With our method, we observed robust results in terms of ac-curacy for a four-labels classification problem, with limited available data. Through feature importance investigation, we conclude that topological descriptors are able to extract useful discriminative information and provide novel insights.Since our data are restricted to single-patient recordings, generalization might be limited. Nevertheless, our method can be extended and applied to a wide range of neurophysiological recordings and it might be an intriguing point of departure for future studies.
Visually Exploring Multi-Purpose Audio Data
Authors: David Heise, Helen L. Bear
Date: 2021-10-09 00:00:00
Tasks: Acoustic Scene Classification, Classification, Scene Classification

We analyse multi-purpose audio using tools to visualise similarities within the data that may be observed via unsupervised methods. The success of machine learning classifiers is affected by the information contained within system inputs, so we investigate whether latent patterns within the data may explain performance limitations of such classifiers. We use the visual assessment of cluster tendency (VAT) technique on a well known data set to observe how the samples naturally cluster, and we make comparisons to the labels used for audio geotagging and acoustic scene classification. We demonstrate that VAT helps to explain and corroborate confusions observed in prior work to classify this audio, yielding greater insight into the performance - and limitations - of supervised classification systems. While this exploratory analysis is conducted on data for which we know the "ground truth" labels, this method of visualising the natural groupings as dictated by the data leads to important questions about unlabelled data that can help the evaluation and realistic expectations of future (including self-supervised) classification systems.
Embed Everything: A Method for Efficiently Co-Embedding Multi-Modal Spaces
Authors: Sarah Di, Robin Yu, Amol Kapoor
Date: 2021-10-09 00:00:00
Tasks: Transfer Learning

Any general artificial intelligence system must be able to interpret, operate on, and produce data in a multi-modal latent space that can represent audio, imagery, text, and more. In the last decade, deep neural networks have seen remarkable success in unimodal data distributions, while transfer learning techniques have seen a massive expansion of model reuse across related domains. However, training multi-modal networks from scratch remains expensive and illusive, while heterogeneous transfer learning (HTL) techniques remain relatively underdeveloped. In this paper, we propose a novel and cost-effective HTL strategy for co-embedding multi-modal spaces. Our method avoids cost inefficiencies by preprocessing embeddings using pretrained models for all components, without passing gradients through these models. We prove the use of this system in a joint image-audio embedding task. Our method has wide-reaching applications, as successfully bridging the gap between different latent spaces could provide a framework for the promised "universal" embedding.
Multimodal Approach for Assessing Neuromotor Coordination in Schizophrenia Using Convolutional Neural Networks
Authors: Yashish M. Siriwardena, Chris Kitchen, Deanna L. Kelly, Carol Espy-Wilson
Date: 2021-10-09 00:00:00

This study investigates the speech articulatory coordination in schizophrenia subjects exhibiting strong positive symptoms (e.g. hallucinations and delusions), using two distinct channel-delay correlation methods. We show that the schizophrenic subjects with strong positive symptoms and who are markedly ill pose complex articulatory coordination pattern in facial and speech gestures than what is observed in healthy subjects. This distinction in speech coordination pattern is used to train a multimodal convolutional neural network (CNN) which uses video and audio data during speech to distinguish schizophrenic patients with strong positive symptoms from healthy subjects. We also show that the vocal tract variables (TVs) which correspond to place of articulation and glottal source outperform the Mel-frequency Cepstral Coefficients (MFCCs) when fused with Facial Action Units (FAUs) in the proposed multimodal network. For the clinical dataset we collected, our best performing multimodal network improves the mean F1 score for detecting schizophrenia by around 18% with respect to the full vocal tract coordination (FVTC) baseline method implemented with fusing FAUs and MFCCs.
Leveraging Experience in Lazy Search
Authors: Mohak Bhardwaj, Sanjiban Choudhury, Byron Boots, Siddhartha Srinivasa
Date: 2021-10-10 00:00:00
Tasks: Imitation Learning, Motion Planning

Lazy graph search algorithms are efficient at solving motion planning problems where edge evaluation is the computational bottleneck. These algorithms work by lazily computing the shortest potentially feasible path, evaluating edges along that path, and repeating until a feasible path is found. The order in which edges are selected is critical to minimizing the total number of edge evaluations: a good edge selector chooses edges that are not only likely to be invalid, but also eliminates future paths from consideration. We wish to learn such a selector by leveraging prior experience. We formulate this problem as a Markov Decision Process (MDP) on the state of the search problem. While solving this large MDP is generally intractable, we show that we can compute oracular selectors that can solve the MDP during training. With access to such oracles, we use imitation learning to find effective policies. If new search problems are sufficiently similar to problems solved during training, the learned policy will choose a good edge evaluation ordering and solve the motion planning problem quickly. We evaluate our algorithms on a wide range of 2D and 7D problems and show that the learned selector outperforms baseline commonly used heuristics. We further provide a novel theoretical analysis of lazy search in a Bayesian framework as well as regret guarantees on our imitation learning based approach to motion planning.
Learning to Pick at Non-Zero-Velocity from Interactive Demonstrations
Authors: Anna Mészáros, Giovanni Franzese, Jens Kober
Date: 2021-10-09 00:00:00
Tasks: Gaussian Processes

This work investigates how the intricate task of a continuous pick & place (P&P) motion may be learned from humans based on demonstrations and corrections. Due to the complexity of the task, these demonstrations are often slow and even slightly flawed, particularly at moments when multiple aspects (i.e., end-effector movement, orientation, and gripper width) have to be demonstrated at once. Rather than training a person to give better demonstrations, non-expert users are provided with the ability to interactively modify the dynamics of their initial demonstration through teleoperated corrective feedback. This in turn allows them to teach motions outside of their own physical capabilities. In the end, the goal is to obtain a faster but reliable execution of the task. The presented framework learns the desired movement dynamics based on the current Cartesian position with Gaussian Processes (GPs), resulting in a reactive, time-invariant policy. Using GPs also allows online interactive corrections and active disturbance rejection through epistemic uncertainty minimization. The experimental evaluation of the framework is carried out on a Franka-Emika Panda. Tests were performed to determine i) the framework's effectiveness in successfully learning how to quickly pick & place an object, ii) ease of policy correction to environmental changes (i.e., different object sizes and mass), and iii) the framework's usability for non-expert users.
Advancing Momentum Pseudo-Labeling with Conformer and Initialization Strategy
Authors: Yosuke Higuchi, Niko Moritz, Jonathan Le Roux, Takaaki Hori
Date: 2021-10-11 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modelling, speech-recognition, Speech Recognition

Pseudo-labeling (PL), a semi-supervised learning (SSL) method where a seed model performs self-training using pseudo-labels generated from untranscribed speech, has been shown to enhance the performance of end-to-end automatic speech recognition (ASR). Our prior work proposed momentum pseudo-labeling (MPL), which performs PL-based SSL via an interaction between online and offline models, inspired by the mean teacher framework. MPL achieves remarkable results on various semi-supervised settings, showing robustness to variations in the amount of data and domain mismatch severity. However, there is further room for improving the seed model used to initialize the MPL training, as it is in general critical for a PL-based method to start training from high-quality pseudo-labels. To this end, we propose to enhance MPL by (1) introducing the Conformer architecture to boost the overall recognition accuracy and (2) exploiting iterative pseudo-labeling with a language model to improve the seed model before applying MPL. The experimental results demonstrate that the proposed approaches effectively improve MPL performance, outperforming other PL-based methods. We also present in-depth investigations to make our improvements effective, e.g., with regard to batch normalization typically used in Conformer and LM quality.
DITTO: Data-efficient and Fair Targeted Subset Selection for ASR Accent Adaptation
Authors: Suraj Kothawade, Anmol Mekala, Chandra Sekhara D, Mayank Kothyari, Rishabh Iyer, Ganesh Ramakrishnan, Preethi Jyothi
Date: 2021-10-10 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

State-of-the-art Automatic Speech Recognition (ASR) systems are known to exhibit disparate performance on varying speech accents. To improve performance on a specific target accent, a commonly adopted solution is to finetune the ASR model using accent-specific labeled speech. However, acquiring large amounts of labeled speech for specific target accents is challenging. Choosing an informative subset of speech samples that are most representative of the target accents becomes important for effective ASR finetuning. To address this problem, we propose DITTO (Data-efficient and faIr Targeted subseT selectiOn) that uses Submodular Mutual Information (SMI) functions as acquisition functions to find the most informative set of utterances matching a target accent within a fixed budget. An important feature of DITTO is that it supports fair targeting for multiple accents, i.e. it can automatically select representative data points from multiple accents when the ASR model needs to perform well on more than one accent. We show that DITTO is 3-5 times more label-efficient than other speech selection methods on the IndicTTS and L2 datasets.
Direct source and early reflections localization using deep deconvolution network under reverberant environment
Authors: Shan Gao, Xihong Wu, Tianshu Qu
Date: 2021-10-10 00:00:00

This paper proposes a deconvolution-based network (DCNN) model for DOA estimation of direct source and early reflections under reverberant scenarios. Considering that the first-order reflections of the sound source also contain spatial directivity like the direct source, we treat both of them as the sources in the learning process. We use the covariance matrix of high order Ambisonics (HOA) signals in the time domain as the input feature of the network, which is concise while containing precise spatial information under reverberant scenarios. Besides, we use the deconvolution-based network for the spatial pseudo-spectrum (SPS) reconstruction in the 2D polar space, based on which the spatial relationship between elevation and azimuth can be depicted. We have carried out a series of experiments based on simulated and measured data under different reverberant scenarios, which prove the robustness and accuracy of the proposed DCNN model.
Stepwise-Refining Speech Separation Network via Fine-Grained Encoding in High-order Latent Domain
Authors: Zengwei Yao, Wenjie Pei, Fanglin Chen, Guangming Lu, David Zhang
Date: 2021-10-10 00:00:00
Tasks: speech-recognition, Speech Recognition, Speech Separation

The crux of single-channel speech separation is how to encode the mixture of signals into such a latent embedding space that the signals from different speakers can be precisely separated. Existing methods for speech separation either transform the speech signals into frequency domain to perform separation or seek to learn a separable embedding space by constructing a latent domain based on convolutional filters. While the latter type of methods learning an embedding space achieves substantial improvement for speech separation, we argue that the embedding space defined by only one latent domain does not suffice to provide a thoroughly separable encoding space for speech separation. In this paper, we propose the Stepwise-Refining Speech Separation Network (SRSSN), which follows a coarse-to-fine separation framework. It first learns a 1-order latent domain to define an encoding space and thereby performs a rough separation in the coarse phase. Then the proposed SRSSN learns a new latent domain along each basis function of the existing latent domain to obtain a high-order latent domain in the refining phase, which enables our model to perform a refining separation to achieve a more precise speech separation. We demonstrate the effectiveness of our SRSSN by conducting extensive experiments, including speech separation in a clean (noise-free) setting on WSJ0-2/3mix datasets as well as in noisy/reverberant settings on WHAM!/WHAMR! datasets. Furthermore, we also perform experiments of speech recognition on separated speech signals by our model to evaluate the performance of speech separation indirectly.