Publications

Identification of Out-of-Distribution Cases of CNN using Class-Based Surprise Adequacy

Mira Marhaba

Ettore Merlo

Giuliano Antoniol

Machine learning is vulnerable to possible incorrect classification of cases that are out of the distribution observed during training and c… (voir plus)alibration

2022-05-01

2022 IEEE/ACM 1st International Conference on AI Engineering – Software Engineering for AI (CAIN) (published)

doi.org

Identification of Out-of-Distribution Cases of CNN using Class-Based Surprise Adequacy

Mira Marhaba

Ettore Merlo

Foutse Khomh

Giuliano Antoniol

Machine learning is vulnerable to possible incorrect classification of cases that are out of the distribution observed during training and c… (voir plus)alibration

2022-05-01

2022 IEEE/ACM 1st International Conference on AI Engineering – Software Engineering for AI (CAIN) (publié)

doi.org

Image Retrieval from Contextual Descriptions

Benno Krojer

Vaibhav Adlakha

Vibhav Vineet

Yash Goyal

Edoardo Ponti

Siva Reddy

The ability to integrate context, including perceptual and temporal cues, plays a pivotal role in grounding the meaning of a linguistic utte… (voir plus)rance. In order to measure to what extent current vision-and-language models master this ability, we devise a new multimodal challenge, Image Retrieval from Contextual Descriptions (ImageCoDe). In particular, models are tasked with retrieving the correct image from a set of 10 minimally contrastive candidates based on a contextual description.As such, each description contains only the details that help distinguish between images.Because of this, descriptions tend to be complex in terms of syntax and discourse and require drawing pragmatic inferences. Images are sourced from both static pictures and video frames.We benchmark several state-of-the-art models, including both cross-encoders such as ViLBERT and bi-encoders such as CLIP, on ImageCoDe.Our results reveal that these models dramatically lag behind human performance: the best variant achieves an accuracy of 20.9 on video frames and 59.4 on static pictures, compared with 90.8 in humans.Furthermore, we experiment with new model variants that are better equipped to incorporate visual and temporal context into their representations, which achieve modest gains. Our hope is that ImageCoDE will foster progress in grounded language understanding by encouraging models to focus on fine-grained visual differences.

2022-05-01

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (publié)

doi.org

arxiv.org

Local Structure Matters Most: Perturbation Study in NLU

Louis Clouâtre

Prasanna Parthasarathi

Amal Zouaq

Sarath Chandar

Recent research analyzing the sensitivity of natural language understanding models to word-order perturbations has shown that neural models … (voir plus)are surprisingly insensitive to the order of words.In this paper, we investigate this phenomenon by developing order-altering perturbations on the order of words, subwords, and characters to analyze their effect on neural models’ performance on language understanding tasks.We experiment with measuring the impact of perturbations to the local neighborhood of characters and global position of characters in the perturbed texts and observe that perturbation functions found in prior literature only affect the global ordering while the local ordering remains relatively unperturbed.We empirically show that neural models, invariant of their inductive biases, pretraining scheme, or the choice of tokenization, mostly rely on the local structure of text to build understanding and make limited use of the global structure.

2022-05-01

Findings of the Association for Computational Linguistics: ACL 2022 (publié)

doi.org

arxiv.org

Moving shared decision making forward in Iran.

Samira Abbasgholizadeh-Rahimi

Nam Nguyen

Mahasti Alizadeh

Dan Poenaru

2022-05-01

Zeitschrift fur Evidenz, Fortbildung und Qualitat im Gesundheitswesen (publié)

doi.org

OptiMaP: swarm-powered Optimized 3D Mapping Pipeline for emergency response operations

Leandro Rincon Costa

Daniel Aloise

Luca Giovanni Gianoli

Andrea Lodi

A smart application in sensing is mainly powered by a two-stage process comprising sensing (collect data) and computing (process data). Whil… (voir plus)e the sensing stage is typically performed locally through a dedicated Internet of Things infrastructure, the computing stage may require a powerful infrastructure in the cloud. However, when connectivity is poor and low latency becomes a requirement — as in emergency response and disaster relief operations — edge computing and ad hoc cloud paradigms come in support to keep the computing stage locally. Being local network connectivity and data processing limited, it is vital to properly optimize how the computing workload will be consumed by the local ad hoc cloud. For this purpose, we present and evaluate the swarm-powered Optimized 3D Mapping Pipeline (OptiMaP) for emergency response 3D mapping missions, which is implemented as a collaborative embedded Robot Operating System (ROS) application integrating an ad hoc telecommunication middleware.We simulate — with Software-In-The-Loop — realistic 3D mapping missions comprising up to 5 drones and 363 images covering 0.293km2. We show how the completion times of mapping missions carried out in a typical centralized manner can be dramatically reduced by two versions of the OptiMaP framework powered, respectively, by a variable neighborhood search heuristic and a greedy method.

2022-05-01

International Conference on Distributed Computing in Sensor Systems (published)

doi.org

P397. Genomic Deletions and Duplications Show Mirror Effects on Cognitive Ability According to Spatial Patterns of Gene Expression in the Human Brain

Kuldeep Kumar

Sayeh Kazem

Guillaume Huguet

Elise Douard

Zohra Saci

Laura Almasy

David C. Glahn

Guillaume Dumas

Sébastien Jacquemont

2022-05-01

Biological Psychiatry (publié)

doi.org

Performance-gated deliberation: A context-adapted strategy in which urgency is opportunity cost

Maximilian Puelma Touzel

Paul Cisek

Guillaume Lajoie

Finding the right amount of deliberation, between insufficient and excessive, is a hard decision making problem that depends on the value we… (voir plus) place on our time. Average-reward, putatively encoded by tonic dopamine, serves in existing reinforcement learning theory as the opportunity cost of time, including deliberation time. Importantly, this cost can itself vary with the environmental context and is not trivial to estimate. Here, we propose how the opportunity cost of deliberation can be estimated adaptively on multiple timescales to account for non-stationary contextual factors. We use it in a simple decision-making heuristic based on average-reward reinforcement learning (AR-RL) that we call Performance-Gated Deliberation (PGD). We propose PGD as a strategy used by animals wherein deliberation cost is implemented directly as urgency, a previously characterized neural signal effectively controlling the speed of the decision-making process. We show PGD outperforms AR-RL solutions in explaining behaviour and urgency of non-human primates in a context-varying random walk prediction task and is consistent with relative performance and urgency in a context-varying random dot motion task. We make readily testable predictions for both neural activity and behaviour.

2022-05-01

PLoS Comput. Biol. (published)

doi.org

Performance-gated deliberation: A context-adapted strategy in which urgency is opportunity cost

Maximilian Puelma Touzel

Paul Cisek

Guillaume Lajoie

2022-05-01

PLoS Comput. Biol. (publié)

doi.org

Predicting the probability distribution of bus travel time to measure the reliability of public transport services

L. Ricard