Publications

Continual Learning with Foundation Models: An Empirical Study of Latent Replay
Oleksiy Ostapenko
Timothee LESORT
Pau Rodriguez
Md Rifat Arefin
Arthur Douillard
Irina Rish
Laurent Charlin
2022-01-01
CoLLAs (publié)
arxiv.org
arxiv.org
Deposited in DRO : 17 January 2022 Version of attached le : Accepted Version Peer-review status of attached
Nelly Bencomo
Jin Guo
Rachel Harrison
Hans-Martin Heyn
Tim Menzies
Much has been written about the algorithmic role that AI plays for automation in SE. But what about the role of AI, augmented by human knowl… (voir plus)edge? Can we make a profound advance by combining human and artificial intelligence? Researchers in requirements engineering think so, arguing that requirement engineering is the secret weapon for better AI and better software. Much has been written about the algorithmic role that AI plays for automation in SE. But what about the role of AI, augmented by human knowledge? Can we make a profound advance by combining human and artificial intelligence? Researchers in requirements engineering think so, arguing that requirement engineering is the secret weapon for better AI and better software1. To begin, we first need a definition. What is requirements engineering or RE? RE used to be viewed as an early lifecycle activity that proceeded analysis, design, coding and testing. For safety critical applications there is certainly a pressing need to create those requirements before the coding starts (we will return to this point, later in the paper). However, in this age of DevOps and Autonomous and Self-adaptive systems, requirements can happen at many other times in a software project[15], [14]. We say that: Requirements engineering is any discussion about what to build and how to trade-off competing cost/benefits. It can happen before, during, or after runtime. 1This paper is based on the Panel “Artificial Intelligence and Requirement Engineering: Challenges and Opportunities”, which took place at the Eighth International Workshop on Artificial Intelligence and Requirements Engineering (AIRE). As shown in Table 1 and Table 2, there are many ways AI can help RE, across a broad range of SE activities. But, what about the other way around? If we add more requirements into AI, and use RE methods to get truly desired requirements, can we make better software by combining human and artificial intelligence? In our view, when integrating AI into software engineering is a co-design problem between humans, the AI model, the data required to train and validate the desired behaviour, and the hardware running the AI model, in addition to the classical software components. This means that when integrating AI, you need to know and understand the context of the system in which you want to apply your AI model to derive the necessary model requirements [17]. For example, in the arena of safety critical systems, model construction must be guided by safety requirements. one challenge for AI in RE are safety standards that base on the EN-IEC 61508 standard2. These safety standards assume that for software only systematic faults exists. Therefore, they emphasise correct processes and the creation of lifecycle artifacts to minimise systematic mistakes during both the 2Functional Safety of Electrical/Electronic/Programmable Electronic Safety-related Systems; for example ISO 26262 for the automotive sector or IEC 61511 for the process industry. IEEE Software (submitted) Published by the IEEE Computer Society © 2021 IEEE 1
2022-01-01
(publié)
www.semanticscholar.org
Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA
Sébastien Lachapelle
Pau Rodriguez
Yash Sharma
Katie E Everett
Rémi LE PRIOL
Alexandre Lacoste
Simon Lacoste-Julien
This work introduces a novel principle we call disentanglement via mechanism sparsity regularization, which can be applied when the latent f… (voir plus)actors of interest depend sparsely on past latent factors and/or observed auxiliary variables. We propose a representation learning method that induces disentanglement by simultaneously learning the latent factors and the sparse causal graphical model that relates them. We develop a rigorous identifiability theory, building on recent nonlinear independent component analysis (ICA) results, that formalizes this principle and shows how the latent variables can be recovered up to permutation if one regularizes the latent mechanisms to be sparse and if some graph connectivity criterion is satisfied by the data generating process. As a special case of our framework, we show how one can leverage unknown-target interventions on the latent factors to disentangle them, thereby drawing further connections between ICA and causality. We propose a VAE-based method in which the latent mechanisms are learned and regularized via binary masks, and validate our theory by showing it learns disentangled representations in simulations.
2022-01-01
CLeaR (publié)
proceedings.mlr.press
openreview.net
DsMLP: A Learning-Based Multi-Layer Perception for MIMO Detection Implemented by Dynamic Stochastic Computing
Qidie Wu
Jinsheng Kuang
Jiyun Tao
Jienan Chen
Warren Gross
As the number of antennas increases in multi-input and multi-output (MIMO) systems, even linear detection methods suffer from sharply increa… (voir plus)sing complexity. This paper proposes a learning-based multi-layer perception (MLP), named dynamic stochastic multi-layer perception (DsMLP), which is implemented by dynamic stochastic computing (DSC). We first establish a similar form between the MLP structure and minimum mean square error (MMSE) matrix operations. Consequently, DsMLP transforms the complex computation problem into an optimization problem of MLP training. Due to the specific design of MLP structure, e.g., same input/output dimension and single layer without activation function, the mathematical representation of DsMLP is identical to the MMSE matrix operations. Therefore, DsMLP guarantees sound model explainability in mathematics, fast convergence in training, and low complexity in computation. Furthermore, we transform the MLP training process to the DSC domain and propose a hardware-efficient scheme for DsMLP. Compared with other state-of-the-art MIMO detectors, DsMLP achieves 1.2× energy efficiency and 1.74× area efficiency.
2022-01-01
IEEE Transactions on Signal Processing (publié)
doi.org
DsMLP: A Learning-Based Multi-Layer Perception for MIMO Detection Implemented by Dynamic Stochastic Computing
Qidie Wu
Jinsheng Kuang
Jiyun Tao
Jienan Chen
Warren Gross
As the number of antennas increases in multi-input and multi-output (MIMO) systems, even linear detection methods suffer from sharply increa… (voir plus)sing complexity. This paper proposes a learning-based multi-layer perception (MLP), named dynamic stochastic multi-layer perception (DsMLP), which is implemented by dynamic stochastic computing (DSC). We first establish a similar form between the MLP structure and minimum mean square error (MMSE) matrix operations. Consequently, DsMLP transforms the complex computation problem into an optimization problem of MLP training. Due to the specific design of MLP structure, e.g., same input/output dimension and single layer without activation function, the mathematical representation of DsMLP is identical to the MMSE matrix operations. Therefore, DsMLP guarantees sound model explainability in mathematics, fast convergence in training, and low complexity in computation. Furthermore, we transform the MLP training process to the DSC domain and propose a hardware-efficient scheme for DsMLP. Compared with other state-of-the-art MIMO detectors, DsMLP achieves 1.2× energy efficiency and 1.74× area efficiency.
2022-01-01
IEEE Transactions on Signal Processing (published)
doi.org
Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking
Yifei Li
Pratheeksha Nair
Kellin Pelrine
Reihaneh Rabbany
2022-01-01
Findings (publié)
doi.org
Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking
Yifei Li
Pratheeksha Nair
Kellin Pelrine
Reihaneh Rabbany
Online escort advertisement websites are widely used for advertising victims of human trafficking. Domain experts agree that advertising mul… (voir plus)tiple people in the same ad is a strong indicator of trafficking. Thus, extracting person names from the text of these ads can provide valuable clues for further analysis. However, Named-Entity Recognition (NER) on escort ads is challenging because the text can be noisy, colloquial and often lacking proper grammar and punctuation. Most existing state-of-the-art NER models fail to demonstrate satisfactory performance in this task. In this paper, we propose NEAT (Name Extraction Against Trafficking) for extracting person names. It effectively combines classic rule-based and dictionary extractors with a contextualized language model to capture ambiguous names (e.g penny, hazel) and adapts to adversarial changes in the text by expanding its dictionary. NEAT shows 19% improvement on average in the F1 classification score for name extraction compared to previous state-of-the-art in two domain-specific datasets.
2022-01-01
Findings (published)
doi.org
Few-Shot Pidgin Text Adaptation via Contrastive Fine-Tuning
Ernie Chang
Jesujoba Oluwadara Alabi
David Ifeoluwa Adelani
Vera Demberg
The surging demand for multilingual dialogue systems often requires a costly labeling process for each language addition. For low resource l… (voir plus)anguages, human annotators are continuously tasked with the adaptation of resource-rich language utterances for each new domain. However, this prohibitive and impractical process can often be a bottleneck for low resource languages that are still without proper translation systems nor parallel corpus. In particular, it is difficult to obtain task-specific low resource language annotations for the English-derived creoles (e.g. Nigerian and Cameroonian Pidgin). To address this issue, we utilize the pretrained language models i.e. BART which has shown great potential in language generation/understanding – we propose to finetune the BART model to generate utterances in Pidgin by leveraging the proximity of the source and target languages, and utilizing positive and negative examples in constrastive training objectives. We collected and released the first parallel Pidgin-English conversation corpus in two dialogue domains and showed that this simple and effective technique is suffice to yield impressive results for English-to-Pidgin generation, which are two closely-related languages.
2022-01-01
COLING (publié)
dblp.uni-trier.de
Findings of the WMT’22 Shared Task on Large-Scale Machine Translation Evaluation for African Languages
David Ifeoluwa Adelani
Md Mahfuz Ibn Alam
Antonios Anastasopoulos
Akshita Bhagia
Marta R. Costa-jussa
Jesse Dodge
Fahim Faisal
Christian Federmann
Natalia N. Fedorova
Francisco S. Guzm'an
Sergey Koshelev
Jean Maillard
Vukosi Marivate
Jonathan Mbuya
Alexandre Mourachko
Safiyyah Saleem
Holger Schwenk
Guillaume Wenzek
We present the results of the WMT’22 SharedTask on Large-Scale Machine Translation Evaluation for African Languages. The shared taskinclud… (voir plus)ed both a data and a systems track, alongwith additional innovations, such as a focus onAfrican languages and extensive human evaluation of submitted systems. We received 14system submissions from 8 teams, as well as6 data track contributions. We report a largeprogress in the quality of translation for Africanlanguages since the last iteration of this sharedtask: there is an increase of about 7.5 BLEUpoints across 72 language pairs, and the average BLEU scores went from 15.09 to 22.60.
2022-01-01
Conference on Machine Translation (publié)
dblp.uni-trier.de
A general class of surrogate functions for stable and efficient reinforcement learning
Sharan Vaswani
Olivier Bachem
Simone Totaro
Robert Lynn Mueller
Shivam Garg
Matthieu Geist
Marlos C. Machado
Pablo Samuel Castro
Nicolas Le Roux
2022-01-01
AISTATS (publié)
proceedings.mlr.press
arxiv.org
GitHub repositories with links to academic papers: Public access, traceability, and evolution
Supatsara Wattanakriengkrai
Bodin Chinthanet
Hideaki Hata
Raula Gaikovina Kula
Christoph Treude
Jin Guo
Kenichi Matsumoto
2022-01-01
Journal of Systems and Software (publié)
doi.org
arxiv.org
Goal-driven optimization of single-neuron properties in artificial networks reveals regularization role of neural diversity and adaptation in the brain
Victor Geadah
Stefan Horoi
Giancarlo Kerg
Guy Wolf
Guillaume Lajoie
Neurons in the brain have rich and adaptive input-output properties. Features such as diverse f-I curves and spike frequency adaptation are … (voir plus)known to place single neurons in optimal coding regimes when facing changing stimuli. Yet, it is still unclear how brain circuits exploit single neuron flexibility, and how network-level requirements may have shaped such cellular function. To answer this question, a multi-scaled approach is needed where the computations of single neurons and of neural circuits must be considered as a complete system. In this work, we use artificial neural networks to systematically investigate single neuron input-output adaptive mechanisms, optimized in an end-to-end fashion. Throughout the optimization process, each neuron has the liberty to modify its nonlinear activation function, parametrized to mimic f-I curves of biological neurons, and to learn adaptation strategies to modify activation functions in real-time during a task. We find that such networks show much-improved robustness to noise and changes in input statistics. Importantly, we find that this procedure recovers precise coding strategies found in biological neurons, such as gain scaling and fractional order differentiation/integration. Using tools from dynamical systems theory, we analyze the role of these emergent single neuron properties and argue that neural diversity and adaptation plays an active regularization role that enables neural circuits to optimally propagate information across time.
2022-01-01
(published)
www.semanticscholar.org