Publications

Inertia-Based Indices to Determine the Number of Clusters in K-Means: An Experimental Evaluation

Andrei Rykov

Renato Cordeiro De Amorim

Boris Mirkin

This paper gives an experimentally supported review and comparison of several indices based on the conventional K-means inertia criterion fo… (voir plus)r determining the number of clusters,

2024-01-01

IEEE Access (publié)

doi.org

Interacting with a Visuotactile Countertop

M. Jenkin

Francois R. Hogan

Kaleem Siddiqi

Jean-François Tremblay

Bobak H. Baghi

Gregory Dudek

2024-01-01

International Conference on Robotics, Computer Vision and Intelligent Systems (publié)

doi.org

INViTE: INterpret and Control Vision-Language Models with Text Explanations

Haozhe Chen

Junfeng Yang

Carl Vondrick

Chengzhi Mao

Columbia University

M. University

Large-scale pre-trained vision foundation models, such as CLIP, have become de facto backbones for various vision tasks. However, due to the… (voir plus)ir black-box nature, understanding the underlying rules behind these models’ predictions and controlling model behaviors have remained open challenges. We present INViTE: a framework for INterpreting Vision Transformer’s latent tokens with Text Explanations. Given a latent token, INViTE retains its semantic information to the final layer using transformer’s local operations and retrieves the closest text for explanation. INViTE enables understanding of model visual reasoning procedure without needing additional model training or data collection. Based on the obtained interpretations, INViTE allows for model editing that controls model reasoning behaviors and improves model robustness against biases and spurious correlations. Our code is available at https://github.com/tonychenxyz/vit-interpret.

2024-01-01

International Conference on Learning Representations (publié)

openreview.net

ÌròyìnSpeech: A multi-purpose Yorùbá Speech Corpus

Tolúlope' Ògúnremí

Kọ́lá Túbọ̀sún

Aremu Anuoluwapo

Iroro Orife

David Ifeoluwa Adelani

2024-01-01

LREC/COLING (publié)

doi.org

arxiv.org

Learnable Filters for Geometric Scattering Modules

Alexander Tong

Frederik Wenkel

Dhananjay Bhaskar

Kincaid MacDonald

Jackson Grady

Michael Perlmutter

Smita Krishnaswamy

Guy Wolf

2024-01-01

IEEE Transactions on Signal Processing (publié)

doi.org

arxiv.org

Learning Conditional Policies for Crystal Design Using Offline Reinforcement Learning

Prashant Govindarajan

Santiago Miret

Jarrid Rector-Brooks

Mariano Phielipp

Janarthanan Rajendran

Sarath Chandar Anbil Parthipan

Navigating through the exponentially large chemical space to search for desirable materials is an extremely challenging task in material dis… (voir plus)covery. Recent developments in generative and geometric deep learning have shown...

2024-01-01

Digital Discovery (publié)

doi.org

openreview.net

Learning to repeatedly solve routing problems

Mouad Morabit

Guy Desaulniers

Andrea Lodi

In the last years, there has been a great interest in machine‐learning‐based heuristics for solving NP‐hard combinatorial optimization… (voir plus) problems. The developed methods have shown potential on many optimization problems. In this paper, we present a learned heuristic for the reoptimization of a problem after a minor change in its data. We focus on the case of the capacited vehicle routing problem with static clients (i.e., same client locations) and changed demands. Given the edges of an original solution, the goal is to predict and fix the ones that have a high chance of remaining in an optimal solution after a change of client demands. This partial prediction of the solution reduces the complexity of the problem and speeds up its resolution, while yielding a good quality solution. The proposed approach resulted in solutions with an optimality gap ranging from 0% to 1.7% on different benchmark instances within a reasonable computing time.

2024-01-01

Networks (publié)

doi.org

arxiv.org

Learning Tabu Search Algorithms: A Scheduling Application

Nazgol Niroumandrad

Nadia Lahrichi

Andrea Lodi

. Metaheuristics are widely recognized as efficient approaches for many combinatorial problems. Studies to improve the performance of metahe… (voir plus)uristics have increasingly relied on the use of various methods either combining different metaheuristics or methods originating outside of the metaheuristic field. This paper presents a learning algorithm to improve tabu search by reducing its search space and the evaluation effort. We study the performance of a learning tabu search algorithm using classification methods in an attempt to select moves through the search space more wisely. The experimental results demonstrate the benefit of using a learning mechanism under deterministic and stochastic conditions.

2024-01-01

Comput. Oper. Res. (publié)

doi.org

Learning the Game: Decoding the Differences between Novice and Expert Players in a Citizen Science Game with Millions of Players

Eddie Cai

Roman Sarrazin-Gendron

Renata Mutalova

Parham Ghasemloo Gheidari

Alexander Butyaev

Gabriel Richard

Sébastien Caisse

Rob Knight

Mathieu Blanchette

Attila Szantner

Jérôme Waldispühl

2024-01-01

FDG (publié)

doi.org

List Comprehension Versus for Loops Performance in Real Python Projects: Should we Care?

Cyrine Zid

François Belias

Massimiliano Di Penta

Foutse Khomh

Giuliano Antoniol

List comprehensions are a Pythonic functional construct allowing developers to express in a concise way loops to build and manipulate lists.… (voir plus) Previous studies point to a gain in speed when list comprehensions are adopted. This paper reports the results of a study that compares the execution time performance of Python code written using list comprehensions as opposed to equivalent imperative programming. To this aim, we have developed a set of transformation rules to map Python for loops into list comprehensions. On the one hand, on artificial code snippets, we found list comprehensions to be faster than procedural code, with differences becoming evident if amplifying the tests, i.e., executing the code fragment thousands of times. On the other hand, this does not happen when executing real-world Python projects, where the performance may or may not improve, depending on the projects' features and the nature of the manipulated objects.

2024-01-01

SANER (publié)

doi.org

Maximum entropy GFlowNets with soft Q-learning

Sobhan Mohammadpour

Emmanuel Bengio

Emma Frejinger

Pierre-Luc Bacon

2024-01-01

AISTATS (publié)

doi.org

arxiv.org