pwc-archive/papers-with-abstracts huggingface dataset:
MLPro: A System for Hosting Crowdsourced Machine Learning Challenges for Open-Ended Research Problems
Authors: Peter Washington, Aayush Nandkeolyar, Sam Yang
Date: 2022-04-04 00:00:00
Tasks: Dimensionality Reduction, Feature Engineering, Imputation

The task of developing a machine learning (ML) model for a particular problem is inherently open-ended, and there is an unbounded set of possible solutions. Steps of the ML development pipeline, such as feature engineering, loss function specification, data imputation, and dimensionality reduction, require the engineer to consider an extensive and often infinite array of possibilities. Successfully identifying high-performing solutions for an unfamiliar dataset or problem requires a mix of mathematical prowess and creativity applied towards inventing and repurposing novel ML methods. Here, we explore the feasibility of hosting crowdsourced ML challenges to facilitate a breadth-first exploration of open-ended research problems, thereby expanding the search space of problem solutions beyond what a typical ML team could viably investigate. We develop MLPro, a system which combines the notion of open-ended ML coding problems with the concept of an automatic online code judging platform. To conduct a pilot evaluation of this paradigm, we crowdsource several open-ended ML challenges to ML and data science practitioners. We describe results from two separate challenges. We find that for sufficiently unconstrained and complex problems, many experts submit similar solutions, but some experts provide unique solutions which outperform the "typical" solution class. We suggest that automated expert crowdsourcing systems such as MLPro have the potential to accelerate ML engineering creativity.
An Analysis of Semantically-Aligned Speech-Text Embeddings
Authors: Muhammad Huzaifah, Ivan Kukanov
Date: 2022-04-04 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modelling, Retrieval, speech-recognition, Speech Recognition, Transfer Learning, zero-shot-classification, Zero-Shot Learning

Embeddings play an important role in end-to-end solutions for multi-modal language processing problems. Although there has been some effort to understand the properties of single-modality embedding spaces, particularly that of text, their cross-modal counterparts are less understood. In this work, we study some intrinsic properties of a joint speech-text embedding space, constructed by minimizing the distance between paired utterance and transcription inputs in a teacher-student model setup, that are informative for several prominent use cases. We found that incorporating automatic speech recognition through both pretraining and multitask scenarios aid semantic alignment significantly, resulting in more tightly coupled embeddings. To analyse cross-modal embeddings we utilise a quantitative retrieval accuracy metric for semantic alignment, zero-shot classification for generalisability, and probing of the encoders to observe the extent of knowledge transfer from one modality to another.
Into-TTS : Intonation Template Based Prosody Control System
Authors: JIhwan Lee, Joun Yeop Lee, Heejin Choi, Seongkyu Mun, Sangjun Park, Jae-Sung Bae, Chanwoo Kim
Date: 2022-04-04 00:00:00
Tasks: Language Modeling, Language Modelling

Intonations play an important role in delivering the intention of a speaker. However, current end-to-end TTS systems often fail to model proper intonations. To alleviate this problem, we propose a novel, intuitive method to synthesize speech in different intonations using predefined intonation templates. Prior to TTS model training, speech data are grouped into intonation templates in an unsupervised manner. Two proposed modules are added to the end-to-end TTS framework: an intonation predictor and an intonation encoder. The intonation predictor recommends a suitable intonation template to the given text. The intonation encoder, attached to the text encoder output, synthesizes speech abiding the requested intonation template. Main contributions of our paper are: (a) an easy-to-use intonation control system covering a wide range of users; (b) better performance in wrapping speech in a requested intonation with improved objective and subjective evaluation; and (c) incorporating a pre-trained language model for intonation modelling. Audio samples are available at https://srtts.github.io/IntoTTS.
MOSRA: Joint Mean Opinion Score and Room Acoustics Speech Quality Assessment
Authors: Karl El Hajal, Milos Cernak, Pablo Mainar
Date: 2022-04-04 00:00:00

The acoustic environment can degrade speech quality during communication (e.g., video call, remote presentation, outside voice recording), and its impact is often unknown. Objective metrics for speech quality have proven challenging to develop given the multi-dimensionality of factors that affect speech quality and the difficulty of collecting labeled data. Hypothesizing the impact of acoustics on speech quality, this paper presents MOSRA: a non-intrusive multi-dimensional speech quality metric that can predict room acoustics parameters (SNR, STI, T60, DRR, and C50) alongside the overall mean opinion score (MOS) for speech quality. By explicitly optimizing the model to learn these room acoustics parameters, we can extract more informative features and improve the generalization for the MOS task when the training data is limited. Furthermore, we also show that this joint training method enhances the blind estimation of room acoustics, improving the performance of current state-of-the-art models. An additional side-effect of this joint prediction is the improvement in the explainability of the predictions, which is a valuable feature for many applications.
Training Fully Connected Neural Networks is $\exists\mathbb{R}$-Complete
Authors: Daniel Bertschinger, Christoph Hertrich, Paul Jungeblut, Tillmann Miltzow, Simon Weber
Date: 2022-04-04 00:00:00
Proceeding: NeurIPS 2023 11

We consider the problem of finding weights and biases for a two-layer fully connected neural network to fit a given set of data points as well as possible, also known as EmpiricalRiskMinimization. Our main result is that the associated decision problem is $\exists\mathbb{R}$-complete, that is, polynomial-time equivalent to determining whether a multivariate polynomial with integer coefficients has any real roots. Furthermore, we prove that algebraic numbers of arbitrarily large degree are required as weights to be able to train some instances to optimality, even if all data points are rational. Our result already applies to fully connected instances with two inputs, two outputs, and one hidden layer of ReLU neurons. Thereby, we strengthen a result by Abrahamsen, Kleist and Miltzow [NeurIPS 2021]. A consequence of this is that a combinatorial search algorithm like the one by Arora, Basu, Mianjy and Mukherjee [ICLR 2018] is impossible for networks with more than one output dimension, unless $\mathsf{NP}=\exists\mathbb{R}$.
Aligned Weight Regularizers for Pruning Pretrained Neural Networks
Authors: James O' Neill, Sourav Dutta, Haytham Assem
Date: 2022-04-04 00:00:00
Proceeding: Findings (ACL) 2022 5
Tasks: Language Modelling, Model Compression

While various avenues of research have been explored for iterative pruning, little is known what effect pruning has on zero-shot test performance and its potential implications on the choice of pruning criteria. This pruning setup is particularly important for cross-lingual models that implicitly learn alignment between language representations during pretraining, which if distorted via pruning, not only leads to poorer performance on language data used for retraining but also on zero-shot languages that are evaluated. In this work, we show that there is a clear performance discrepancy in magnitude-based pruning when comparing standard supervised learning to the zero-shot setting. From this finding, we propose two weight regularizers that aim to maximize the alignment between units of pruned and unpruned networks to mitigate alignment distortion in pruned cross-lingual models and perform well for both non zero-shot and zero-shot settings. We provide experimental results on cross-lingual tasks for the zero-shot setting using XLM-RoBERTa$_{\mathrm{Base}}$, where we also find that pruning has varying degrees of representational degradation depending on the language corresponding to the zero-shot test set. This is also the first study that focuses on cross-lingual language model compression.
Anti-Spoofing Using Transfer Learning with Variational Information Bottleneck
Authors: Youngsik Eom, Yeonghyeon Lee, Ji Sub Um, Hoirin Kim
Date: 2022-04-04 00:00:00
Tasks: Speaker Verification, text-to-speech, Text to Speech, Transfer Learning, Voice Conversion

Recent advances in sophisticated synthetic speech generated from text-to-speech (TTS) or voice conversion (VC) systems cause threats to the existing automatic speaker verification (ASV) systems. Since such synthetic speech is generated from diverse algorithms, generalization ability with using limited training data is indispensable for a robust anti-spoofing system. In this work, we propose a transfer learning scheme based on the wav2vec 2.0 pretrained model with variational information bottleneck (VIB) for speech anti-spoofing task. Evaluation on the ASVspoof 2019 logical access (LA) database shows that our method improves the performance of distinguishing unseen spoofed and genuine speech, outperforming current state-of-the-art anti-spoofing systems. Furthermore, we show that the proposed system improves performance in low-resource and cross-dataset settings of anti-spoofing task significantly, demonstrating that our system is also robust in terms of data size and data distribution.
A single Long Short-Term Memory network for enhancing the prediction of path-dependent plasticity with material heterogeneity and anisotropy
Authors: Ehsan Motevali Haghighi, SeonHong Na
Date: 2022-03-29 00:00:00
Tasks: Inductive Bias

This study presents the applicability of conventional deep recurrent neural networks (RNN) to predict path-dependent plasticity associated with material heterogeneity and anisotropy. Although the architecture of RNN possesses inductive biases toward information over time, it is still challenging to learn the path-dependent material behavior as a function of the loading path considering the change from elastic to elastoplastic regimes. Our attempt is to develop a simple machine-learning-based model that can replicate elastoplastic behaviors considering material heterogeneity and anisotropy. The basic Long-Short Term Memory Unit (LSTM) is adopted for the modeling of plasticity in the two-dimensional space by enhancing the inductive bias toward the past information through manipulating input variables. Our results find that a single LSTM based model can capture the J2 plasticity responses under both monotonic and arbitrary loading paths provided the material heterogeneity. The proposed neural network architecture is then used to model elastoplastic responses of a two-dimensional transversely anisotropic material associated with computational homogenization (FE2). It is also found that a single LSTM model can be used to accurately and effectively capture the path-dependent responses of heterogeneous and anisotropic microstructures under arbitrary mechanical loading conditions.
On scientific understanding with artificial intelligence
Authors: Mario Krenn, Robert Pollice, Si Yue Guo, Matteo Aldeghi, Alba Cervera-Lierta, Pascal Friederich, Gabriel dos Passos Gomes, Florian Häse, Adrian Jinich, AkshatKumar Nigam, Zhenpeng Yao, Alán Aspuru-Guzik
Date: 2022-04-04 00:00:00
Tasks: Philosophy

Imagine an oracle that correctly predicts the outcome of every particle physics experiment, the products of every chemical reaction, or the function of every protein. Such an oracle would revolutionize science and technology as we know them. However, as scientists, we would not be satisfied with the oracle itself. We want more. We want to comprehend how the oracle conceived these predictions. This feat, denoted as scientific understanding, has frequently been recognized as the essential aim of science. Now, the ever-growing power of computers and artificial intelligence poses one ultimate question: How can advanced artificial systems contribute to scientific understanding or achieve it autonomously? We are convinced that this is not a mere technical question but lies at the core of science. Therefore, here we set out to answer where we are and where we can go from here. We first seek advice from the philosophy of science to understand scientific understanding. Then we review the current state of the art, both from literature and by collecting dozens of anecdotes from scientists about how they acquired new conceptual understanding with the help of computers. Those combined insights help us to define three dimensions of android-assisted scientific understanding: The android as a I) computational microscope, II) resource of inspiration and the ultimate, not yet existent III) agent of understanding. For each dimension, we explain new avenues to push beyond the status quo and unleash the full power of artificial intelligence's contribution to the central aim of science. We hope our perspective inspires and focuses research towards androids that get new scientific understanding and ultimately bring us closer to true artificial scientists.
Feasibility of nowcasting SDG indicators: a comprehensive survey
Authors: Daniel Hopp, Emily Fu, Anu Peltola
Date: 2022-03-23 00:00:00
Tasks: Survey

The 2030 Agenda and accompanying Sustainable Development Goals (SDGs) are vital in guiding national and global policy. However, many of the SDG indicators used to measure progress toward those goals suffer from long publication lags. Nowcasting has the potential to address this problem and generate more timely estimates of those indicators. This paper provides resources for achieving that potential by 1) carrying out a comprehensive nowcasting feasibility survey of all SDG indicators to assess their potential to be nowcast, and 2) performing a case study of indicator 9.4.1 to illustrate and shed light on the process of performing a nowcasting exercise. There exist 231 SDG indicators, but due to only examining Tier 1 indicators and the fact that many indicators have multiple sub-indicators, 362 indicators and sub-indicators were eventually surveyed. Of those 362, 150 were found highly likely to be suitable candidates for nowcasting, 87 were found to be likely, and 125 were found to be unsuitable.
Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques
Authors: Luis A. Barboza, Shu-Wei Chou, Paola Vásquez, Yury E. García, Juan G. Calvo, Hugo C. Hidalgo, Fabio Sanchez
Date: 2022-03-23 00:00:00

Dengue fever is a vector-borne disease mostly endemic to tropical and subtropical countries that affect millions every year and is considered a significant burden for public health. Its geographic distribution makes it highly sensitive to climate conditions. Here, we explore the effect of climate variables using the Generalized Additive Model for location, scale, and shape (GAMLSS) and Random Forest (RF) machine learning algorithms. Using the reported number of dengue cases, we obtained reliable predictions. The uncertainty of the predictions was also measured. These predictions will serve as input to health officials to further improve and optimize the allocation of resources prior to dengue outbreaks.
FedRecAttack: Model Poisoning Attack to Federated Recommendation
Authors: Dazhong Rong, Shuai Ye, Ruoyan Zhao, Hon Ning Yuen, Jianhai Chen, Qinming He
Date: 2022-04-01 00:00:00
Tasks: Federated Learning, model, Model Poisoning, Recommendation Systems

Federated Recommendation (FR) has received considerable popularity and attention in the past few years. In FR, for each user, its feature vector and interaction data are kept locally on its own client thus are private to others. Without the access to above information, most existing poisoning attacks against recommender systems or federated learning lose validity. Benifiting from this characteristic, FR is commonly considered fairly secured. However, we argue that there is still possible and necessary security improvement could be made in FR. To prove our opinion, in this paper we present FedRecAttack, a model poisoning attack to FR aiming to raise the exposure ratio of target items. In most recommendation scenarios, apart from private user-item interactions (e.g., clicks, watches and purchases), some interactions are public (e.g., likes, follows and comments). Motivated by this point, in FedRecAttack we make use of the public interactions to approximate users' feature vectors, thereby attacker can generate poisoned gradients accordingly and control malicious users to upload the poisoned gradients in a well-designed way. To evaluate the effectiveness and side effects of FedRecAttack, we conduct extensive experiments on three real-world datasets of different sizes from two completely different scenarios. Experimental results demonstrate that our proposed FedRecAttack achieves the state-of-the-art effectiveness while its side effects are negligible. Moreover, even with small proportion (3%) of malicious users and small proportion (1%) of public interactions, FedRecAttack remains highly effective, which reveals that FR is more vulnerable to attack than people commonly considered.
Deep Learning for Spectral Filling in Radio Frequency Applications
Authors: Matthew Setzler, Elizabeth Coda, Jeremiah Rounds, Michael Vann, Michael Girard
Date: 2022-03-31 00:00:00
Tasks: Deep Learning

Due to the Internet of Things (IoT) proliferation, Radio Frequency (RF) channels are increasingly congested with new kinds of devices, which carry unique and diverse communication needs. This poses complex challenges in modern digital communications, and calls for the development of technological innovations that (i) optimize capacity (bitrate) in limited bandwidth environments, (ii) integrate cooperatively with already-deployed RF protocols, and (iii) are adaptive to the ever-changing demands in modern digital communications. In this paper we present methods for applying deep neural networks for spectral filling. Given an RF channel transmitting digital messages with a pre-established modulation scheme, we automatically learn novel modulation schemes for sending extra information, in the form of additional messages, "around" the fixed-modulation signals (i.e., without interfering with them). In so doing, we effectively increase channel capacity without increasing bandwidth. We further demonstrate the ability to generate signals that closely resemble the original modulations, such that the presence of extra messages is undetectable to third-party listeners. We present three computational experiments demonstrating the efficacy of our methods, and conclude by discussing the implications of our results for modern RF applications.
Introducing ECAPA-TDNN and Wav2Vec2.0 Embeddings to Stuttering Detection
Authors: Shakeel Ahmad Sheikh, Md Sahidullah, Fabrice Hirsch, Slim Ouni
Date: 2022-04-04 00:00:00

The adoption of advanced deep learning (DL) architecture in stuttering detection (SD) tasks is challenging due to the limited size of the available datasets. To this end, this work introduces the application of speech embeddings extracted with pre-trained deep models trained on massive audio datasets for different tasks. In particular, we explore audio representations obtained using emphasized channel attention, propagation, and aggregation-time-delay neural network (ECAPA-TDNN) and Wav2Vec2.0 model trained on VoxCeleb and LibriSpeech datasets respectively. After extracting the embeddings, we benchmark with several traditional classifiers, such as a k-nearest neighbor, Gaussian naive Bayes, and neural network, for the stuttering detection tasks. In comparison to the standard SD system trained only on the limited SEP-28k dataset, we obtain a relative improvement of 16.74% in terms of overall accuracy over baseline. Finally, we have shown that combining two embeddings and concatenating multiple layers of Wav2Vec2.0 can further improve SD performance up to 1% and 2.64% respectively.
Optimising Energy Efficiency in UAV-Assisted Networks using Deep Reinforcement Learning
Authors: Babatunji Omoniwa, Boris Galkin, Ivana Dusparic
Date: 2022-04-04 00:00:00
Tasks: Deep Reinforcement Learning, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

In this letter, we study the energy efficiency (EE) optimisation of unmanned aerial vehicles (UAVs) providing wireless coverage to static and mobile ground users. Recent multi-agent reinforcement learning approaches optimise the system's EE using a 2D trajectory design, neglecting interference from nearby UAV cells. We aim to maximise the system's EE by jointly optimising each UAV's 3D trajectory, number of connected users, and the energy consumed, while accounting for interference. Thus, we propose a cooperative Multi-Agent Decentralised Double Deep Q-Network (MAD-DDQN) approach. Our approach outperforms existing baselines in terms of EE by as much as 55 -- 80%.
More Efficient Identifiability Verification in ODE Models by Reducing Non-Identifiability
Authors: Ilia Ilmer, Alexey Ovchinnikov, Gleb Pogudin, Pedro Soto
Date: 2022-04-04 00:00:00

Structural global parameter identifiability indicates whether one can determine a parameter's value from given inputs and outputs in the absence of noise. If a given model has parameters for which there may be infinitely many values, such parameters are called non-identifiable. We present a procedure for accelerating a global identifiability query by eliminating algebraically independent non-identifiable parameters. Our proposed approach significantly improves performance across different computer algebra frameworks.
Estimating Social Influence from Observational Data
Authors: Dhanya Sridhar, Caterina De Bacco, David Blei
Date: 2022-03-24 00:00:00

We consider the problem of estimating social influence, the effect that a person's behavior has on the future behavior of their peers. The key challenge is that shared behavior between friends could be equally explained by influence or by two other confounding factors: 1) latent traits that caused people to both become friends and engage in the behavior, and 2) latent preferences for the behavior. This paper addresses the challenges of estimating social influence with three contributions. First, we formalize social influence as a causal effect, one which requires inferences about hypothetical interventions. Second, we develop Poisson Influence Factorization (PIF), a method for estimating social influence from observational data. PIF fits probabilistic factor models to networks and behavior data to infer variables that serve as substitutes for the confounding latent traits. Third, we develop assumptions under which PIF recovers estimates of social influence. We empirically study PIF with semi-synthetic and real data from Last.fm, and conduct a sensitivity analysis. We find that PIF estimates social influence most accurately compared to related methods and remains robust under some violations of its assumptions.