Publications

Attention Is All You Need in Speech Separation
Cem Subakan
Mirco Ravanelli
Samuele Cornell
Mirko Bronzi
Jianyuan Zhong
Recurrent Neural Networks (RNNs) have long been the dominant architecture in sequence-to-sequence learning. RNNs, however, are inherently se… (voir plus)quential models that do not allow parallelization of their computations. Transformers are emerging as a natural alternative to standard RNNs, replacing recurrent computations with a multi-head attention mechanism. In this paper, we propose the SepFormer, a novel RNN-free Transformer-based neural network for speech separation. The SepFormer learns short and long-term dependencies with a multi-scale approach that employs transformers. The proposed model achieves state-of-the-art (SOTA) performance on the standard WSJ0-2/3mix datasets. It reaches an SI-SNRi of 22.3 dB on WSJ0-2mix and an SI-SNRi of 19.5 dB on WSJ0-3mix. The SepFormer inherits the parallelization advantages of Transformers and achieves a competitive performance even when downsampling the encoded representation by a factor of 8. It is thus significantly faster and it is less memory-demanding than the latest speech separation systems with comparable performance.
2021-12-31
IEEE International Conference on Acoustics, Speech and Signal Processing (inconnu)
doi.org
arxiv.org
Augmenting Human Selves Through Artificial Agents – Lessons From the Brain
Georg Northoff
Maia Fraser
John Griffiths
Dimitris A. Pinotsis
Prakash Panangaden
Rosalyn Moran
Karl Friston
Much of current artificial intelligence (AI) and the drive toward artificial general intelligence (AGI) focuses on developing machines for f… (voir plus)unctional tasks that humans accomplish. These may be narrowly specified tasks as in AI, or more general tasks as in AGI – but typically these tasks do not target higher-level human cognitive abilities, such as consciousness or morality; these are left to the realm of so-called “strong AI” or “artificial consciousness.” In this paper, we focus on how a machine can augment humans rather than do what they do, and we extend this beyond AGI-style tasks to augmenting peculiarly personal human capacities, such as wellbeing and morality. We base this proposal on associating such capacities with the “self,” which we define as the “environment-agent nexus”; namely, a fine-tuned interaction of brain with environment in all its relevant variables. We consider richly adaptive architectures that have the potential to implement this interaction by taking lessons from the brain. In particular, we suggest conjoining the free energy principle (FEP) with the dynamic temporo-spatial (TSD) view of neuro-mental processes. Our proposed integration of FEP and TSD – in the implementation of artificial agents – offers a novel, expressive, and explainable way for artificial agents to adapt to different environmental contexts. The targeted applications are broad: from adaptive intelligence augmenting agents (IA’s) that assist psychiatric self-regulation to environmental disaster prediction and personal assistants. This reflects the central role of the mind and moral decision-making in most of what we do as humans.
2021-12-31
Frontiers in Computational Neuroscience (publié)
doi.org
Author Correction: Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion
Matthew Farrell
Stefano Recanatesi
Timothy Moore
Guillaume Lajoie
Eric Shea-Brown
2021-12-31
Nature Machine Intelligence (publié)
doi.org
Behind the Machine's Gaze: Biologically Constrained Neural Networks Exhibit Human-like Visual Attention
Leo Schwinn
Doina Precup
B. Eskofier
Dario Zanca
.
2021-12-31
arXiv.org (prépublication)
doi.org
Biasly: a machine learning based platform for automatic racial discrimination detection in online texts
David Bamman
Chris Dyer
Noah A. Smith. 2014
Steven Bird
Ewan Klein
Edward Loper
Nat-527
Jacob Devlin
Ming-Wei Chang
Kenton Lee
Kristina Toutanova. 2019
Bert
Samuel Gehman
Suchin Gururangan
Maarten Sap
Dan Hendrycks
Kevin Gimpel. 2020
Gaussian
Alex Lamb
Di He … (voir 22 de plus)
Anirudh Goyal
Guolin Ke
Feng-Ju Liao
Mirco Ravanaelli
Yoshua Bengio
Zhenzhong Lan
Mingda Chen
Sebastian Goodman
Yann Lecun
Bernhard E. Boser
J. Denker
Don-608 nie Henderson
Robin Howard
Wayne Hubbard
Yinhan Liu
Myle Ott
Naman Goyal
Jingfei Du
Mandar Joshi
Danqi Chen
Omer Levy
Mike Lewis
Warning : this paper contains content that may 001 be offensive or upsetting. 002 Detecting hateful, toxic, and otherwise racist 003 or sexi… (voir plus)st language in user-generated online con-004 tents has become an increasingly important task 005 in recent years. Indeed, the anonymity, the 006 transience, the size of messages, and the dif-007 ficulty of management, facilitate the diffusion 008 of racist or hateful messages across the Inter-009 net. The critical influence of this cyber-racism 010 is no longer limited to social media, but also 011 has a significant effect on our society : corpo-012 rate business operation, users’ health, crimes, 013 etc. Traditional racist speech reporting chan-014 nels have proven inadequate due to the enor-015 mous explosion of information, so there is an 016 urgent need for a method to automatically and 017 promptly detect texts with racial discrimination. 018 We propose in this work, a machine learning-019 based approach to enable automatic detection 020 of racist text content over the internet. State-of-021 the-art machine learning models that are able 022 to grasp language structures are adapted in this 023 study. Our main contribution include 1) a large 024 scale racial discrimination data set collected 025 from three distinct sources and annotated ac-026 cording to a guideline developed by specialists, 027 2) a set of machine learning models with vari-028 ous architectures for racial discrimination de-029 tection, and 3) a web-browser-based software 030 that assist users to debias their texts when us-031 ing the internet. All these resources are made 032 publicly available.
2021-12-31
(publié)
www.semanticscholar.org
Bisimulation metrics and norms for real-weighted automata
Borja Balle
Pascale Gourdeau
Prakash Panangaden
2021-12-31
Information and Computation (publié)
doi.org
Brain-spinal cord interaction in long-term motor sequence learning in human: An fMRI study
Ali Khatibi
Shahabeddin Vahdat
Ovidiu Lungu
Jürgen Finsterbusch
Christian Büchel
Julien Cohen-Adad
Veronique Marchand-Pauvert
Julien Doyon
2021-12-31
NeuroImage (publié)
doi.org
Building Together - Towards a Roadmap for African Language Technologies
Kathleen Siminyu
Jade Abbott
Kọ́lá Túbọ̀sún
Aremu Anuoluwapo
Blessing Kudzaishe Sibanda
Kofi Yeboah
David Ifeoluwa Adelani
Masabata Mokgesi-Selinga
Frederick R. Apina
Angela Thandizwe Mthembu
Arshath Ramkilowan
Babatunde Oladimeji
2021-12-31
Social Science Research Network (publié)
doi.org
CES-KD: Curriculum-based Expert Selection for Guided Knowledge Distillation
Ibtihel Amara
Maryam Ziaeefard
Brett H. Meyer
Warren Gross
James J. Clark
Knowledge distillation (KD) is an effective tool for compressing deep classification models for edge devices. However, the performance of KD… (voir plus) is affected by the large capacity gap between the teacher and student networks. Recent methods have resorted to a multiple teacher assistant (TA) setting for KD, which sequentially decreases the size of the teacher model to relatively bridge the size gap between these models. This paper proposes a new technique called Curriculum Expert Selection for Knowledge Distillation (CES-KD) to efficiently enhance the learning of a compact student under the capacity gap problem. This technique is built upon the hypothesis that a student network should be guided gradually using stratified teaching curriculum as it learns easy (hard) data samples better and faster from a lower (higher) capacity teacher network. Specifically, our method is a gradual TA-based KD technique that selects a single teacher per input image based on a curriculum driven by the difficulty in classifying the image. In this work, we empirically verify our hypothesis and rigorously experiment with CIFAR-10, CIFAR-100, CINIC-10, and ImageNet datasets and show improved accuracy on VGG-like models, ResNets, and WideResNets architectures.
2021-12-31
International Conference on Pattern Recognition (publié)
doi.org
arxiv.org
Cognitive Models as Simulators: The Case of Moral Decision-Making
Ardavan S. Nobandegani
T. Shultz
Irina Rish
2021-12-31
CogSci (publié)
doi.org
arxiv.org
COIL: A Deep Architecture for Column Generation
Behrouz Babaki
Laurent Charlin
Sanjay Dominik Jena
. Column generation is a popular method to solve large-scale linear programs with an exponential number of variables. Several important appl… (voir plus)ications, such as the vehicle routing problem, rely on this technique in order to be solved. However, in practice, column generation methods suffer from slow convergence (i.e. they require too many iterations). Stabilization techniques, which carefully select the column to add at each iteration, are commonly used to improve convergence. In this work, we frame the problem of selecting which columns to add as one of sequential decision-making. We propose a neural column generation architecture that iteratively selects columns to be added to the problem. Our architecture is inspired by stabilization techniques and predicts the optimal duals, which are then used to select the columns to add. We proposed architecture, trained using imitation learning. Exemplified on the Vehicle Routing Problem, we show that several machine learning models yield good performance in predicting the optimal duals and that our architecture outperforms them as well as a popular state-of-the-art stabilization technique. Further, the architecture approach can generalize to instances larger than those observed during training.
2021-12-31
(publié)
www.semanticscholar.org
Combining Reinforcement Learning and Constraint Programming for Sequence-Generation Tasks with Hard Constraints
Daphne Lafleur
A. Chandar
Gilles Pesant
While Machine Learning (ML) techniques are good at generating data similar to a dataset, they lack the capacity to enforce constraints. On t… (voir plus)he other hand, any solution to a Constraint Programming (CP) model satisfies its constraints but has no obligation to imitate a dataset. Yet, we sometimes need both. In this paper we borrow RL-Tuner, a Reinforcement Learning (RL) algorithm introduced to tune neural networks, as our enabling architecture to exploit the respective strengths of ML and CP. RL-Tuner maximizes the sum of a pretrained network’s learned probabilities and of manually-tuned penalties for each violated constraint. We replace the latter with outputs of a CP model representing the marginal probabilities of each value and the number of constraint violations. As was the case for the original RL-Tuner, we apply our algorithm to music generation since it is a highly-constrained domain for which CP is especially suited. We show that combining ML and CP, as opposed to using them individually, allows the agent to reflect the pretrained network while taking into account constraints, leading to melodic lines that respect both the corpus’ style and the music theory constraints.
2021-12-31
International Conference on Principles and Practice of Constraint Programming (publié)
doi.org