Publications

Strong Consistency and Rate of Convergence of Switched Least Squares System Identification for Autonomous Markov Jump Linear Systems

Borna Sayedana

Mohammad Afshari

Peter E. Caines

Aditya Mahajan

In this paper, we investigate the problem of system identification for autonomous Markov jump linear systems (MJS) with complete state obser… (voir plus)vations. We propose switched least squares method for identification of MJS, show that this method is strongly consistent, and derive data-dependent and data-independent rates of convergence. In particular, our data-independent rate of convergence shows that, almost surely, the system identification error is

2023-12-31

IEEE Transactions on Automatic Control (publié)

doi.org

arxiv.org

No such thing as one-size-fits-all in AI ethics frameworks: a comparative case study

Vivian Qiang

Jimin Rhim

AJung Moon

2023-12-31

AI Soc. (publié)

doi.org

TARIC-SLU: A Tunisian Benchmark Dataset for Spoken Language Understanding

Salima Mdhaffar

Fethi Bougares

Renato De Mori

Salah Zaiem

Mirco Ravanaelli

Yannick Estève

In recent years, there has been a significant increase in interest in developing Spoken Language Understanding (SLU) systems. SLU involves e… (voir plus)xtracting a list of semantic information from the speech signal. A major issue for SLU systems is the lack of sufficient amount of bi-modal (audio and textual semantic annotation) training data. Existing SLU resources are mainly available in high-resource languages such as English, Mandarin and French. However, one of the current challenges concerning low-resourced languages is data collection and annotation. In this work, we present a new freely available corpus, named TARIC-SLU, composed of railway transport conversations in Tunisian dialect that is continuously annotated in dialogue acts and slots. We describe the semantic model of the dataset, the data and experiments conducted to build ASR-based and SLU-based baseline models. To facilitate its use, a complete recipe, including data preparation, training and evaluation scripts, has been built and will be integrated to SpeechBrain, a popular open-source conversational AI toolkit based on PyTorch.

2023-12-31

International Conference on Language Resources and Evaluation (publié)

dblp.uni-trier.de

Technical Note—Risk-Averse Regret Minimization in Multistage Stochastic Programs

Mehran Poursoltani

Erick Delage

Angelos Georghiou

2023-12-31

Oper. Res. (publié)

doi.org

On the Identifiability of Quantized Factors

Vitória Barin-Pacela

Kartik Ahuja

Simon Lacoste-Julien

Pascal Vincent

Disentanglement aims to recover meaningful latent ground-truth factors from the observed distribution solely, and is formalized through the … (voir plus)theory of identifiability. The identifiability of independent latent factors is proven to be impossible in the unsupervised i.i.d. setting under a general nonlinear map from factors to observations. In this work, however, we demonstrate that it is possible to recover quantized latent factors under a generic nonlinear diffeomorphism. We only assume that the latent factors have independent discontinuities in their density, without requiring the factors to be statistically independent. We introduce this novel form of identifiability, termed quantized factor identifiability, and provide a comprehensive proof of the recovery of the quantized factors.

2023-12-31

Conference on Causal Learning and Reasoning (publié)

doi.org

proceedings.mlr.press

On the incompatibility of accuracy and equal opportunity

Carlos Pinzón

Catuscia Palamidessi

Pablo Piantanida

Frank Valencia

2023-12-31

Mach. Learn. (publié)

doi.org

On the Limits of Multi-modal Meta-Learning with Auxiliary Task Modulation Using Conditional Batch Normalization

Jordi Armengol-Estapé

Vincent Michalski

Ramnath Kumar

Pierre-Luc St-Charles

Doina Precup

Samira Ebrahimi Kahou

Few-shot learning aims to learn representations that can tackle novel tasks given a small number of examples. Recent studies show that cross… (voir plus)-modal learning can improve representations for few-shot classification. More specifically, language is a rich modality that can be used to guide visual learning. In this work, we experiment with a multi-modal architecture for few-shot learning that consists of three components: a classifier, an auxiliary network, and a bridge network. While the classifier performs the main classification task, the auxiliary network learns to predict language representations from the same input, and the bridge network transforms high-level features of the auxiliary network into modulation parameters for layers of the few-shot classifier using conditional batch normalization. The bridge should encourage a form of lightweight semantic alignment between language and vision which could be useful for the classifier. However, after evaluating the proposed approach on two popular few-shot classification benchmarks we find that a) the improvements do not reproduce across benchmarks, and b) when they do, the improvements are due to the additional compute and parameters introduced by the bridge network. We contribute insights and recommendations for future work in multi-modal meta-learning, especially when using language representations.

2023-12-31

Insights from Negative Results in NLP @ Association for Computational Linguistics (publié)

doi.org

arxiv.org

Towards equilibrium molecular conformation generation with GFlowNets

Alexandra Volokhova

Michał Koziarski

Alex Hernández-García

Cheng-Hao Liu

Santiago Miret

Pablo Lemos

Luca Thiede

Zichao Yan

Alán Aspuru-Guzik

Yoshua Bengio

Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule. In this pa… (voir plus)per we propose to use GFlowNet for sampling conformations of small molecules from the Boltzmann distribution, as determined by the molecule's energy. The proposed approach can be used in combination with energy estimation methods of different fidelity and discovers a diverse set of low-energy conformations for highly flexible drug-like molecules. We demonstrate that GFlowNet can reproduce molecular potential energy surfaces by sampling proportionally to the Boltzmann distribution.

2023-12-31

Digital Discovery (publié)

doi.org

openreview.net

Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching

Naren Wulan

Lijun An

Chen Zhang

Ru Kong

Pansheng Chen

Danilo Bzdok

Simon B. Eickhoff

Avram J. Holmes

B.T. Thomas Yeo

Individualized phenotypic prediction based on structural MRI is an important goal in neuroscience. Prediction performance increases with lar… (voir plus)ger samples, but small-scale datasets with fewer than 200 participants are often unavoidable. We have previously proposed a “meta-matching” framework to translate models trained from large datasets to improve the prediction of new unseen phenotypes in small collection efforts. Meta-matching exploits correlations between phenotypes, yielding large improvement over classical machine learning when applied to prediction models using resting-state functional connectivity as input features. Here, we adapt the two best performing meta-matching variants (“meta-matching finetune” and “meta-matching stacking”) from our previous study to work with T1-weighted MRI data by changing the base neural network architecture to a 3D convolution neural network. We compare the two meta-matching variants with elastic net and classical transfer learning using the UK Biobank (N = 36,461), Human Connectome Project Young Adults (HCP-YA) dataset (N = 1,017) and HCP-Aging dataset (N = 656). We find that meta-matching outperforms elastic net and classical transfer learning by a large margin, both when translating models within the same dataset, as well as translating models across datasets with different MRI scanners, acquisition protocols and demographics. For example, when translating a UK Biobank model to 100 HCP-YA participants, meta-matching finetune yielded a 136% improvement in variance explained over transfer learning, with an average absolute gain of 2.6% (minimum = −0.9%, maximum = 17.6%) across 35 phenotypes. Overall, our results highlight the versatility of the meta-matching framework.

2023-12-31

Imaging Neuroscience (publié)

doi.org

Tree Broad Learning System for Small Data Modeling.

Heng Xia

Jian Tang

Wen Yu

JunFei Qiao

Broad learning system based on neural network (BLS-NN) has poor efficiency for small data modeling with various dimensions. Tree-based BLS (… (voir plus)TBLS) is designed for small data modeling by introducing nondifferentiable modules and an ensemble strategy to the traditional broad learning system (BLS). TBLS replaces the neurons of BLS with the tree modules to map the input data. Moreover, we present three new TBLS variant methods and their incremental learning implementations, which are motivated by deep, broad, and ensemble learning. Their major distinction is reflected in the incremental learning strategies based on: 1) mean square error (mse); 2) pseudo-inverse; and 3) pseudo-inverse theory and stack representation. Therefore, this study further explores the domain of BLS based on the nondifferentiable modules. The simulations are compared with some state-of-the-art (SOTA) BLS-NN and tree methods under high-, medium-, and low-dimensional benchmark datasets. Results show that the proposed method outperforms the BLS-NN, and the modeling accuracy is remarkably improved with the small training data of the proposed TBLS.

2023-12-31

IEEE Trans. Neural Networks Learn. Syst. (publié)

doi.org

Triage Software Update Impact via Release Notes Classification

Solomon Berhe

Vanessa Kan

Omhier Khan

Nathan Pader

Ali Zain Farooqui

Marc Maynard

Foutse Khomh

2023-12-31