Publications

MAP: Model Merging with Amortized Pareto Front Using Limited Computation
Lu Li
Tianyu Zhang
Zhiqi Bu
Suyuchen Wang
Huan He
Jie Fu
Yonghui Wu
Jiang Bian
Yong Chen
Yoshua Bengio
2024-10-01
NeurIPS.cc/2024/Workshop/Federated_Learning (présentation orale)
openreview.net
openreview.net
Single-Shot Learning of Stable Dynamical Systems for Long-Horizon Manipulation Tasks
Alexandre St-Aubin
Amin Abyaneh
Hsiu-Chin Lin
Mastering complex sequential tasks continues to pose a significant challenge in robotics. While there has been progress in learning long-hor… (voir plus)izon manipulation tasks, most existing approaches lack rigorous mathematical guarantees for ensuring reliable and successful execution. In this paper, we extend previous work on learning long-horizon tasks and stable policies, focusing on improving task success rates while reducing the amount of training data needed. Our approach introduces a novel method that (1) segments long-horizon demonstrations into discrete steps defined by waypoints and subgoals, and (2) learns globally stable dynamical system policies to guide the robot to each subgoal, even in the face of sensory noise and random disturbances. We validate our approach through both simulation and real-world experiments, demonstrating effective transfer from simulation to physical robotic platforms. Code is available at https://github.com/Alestaubin/stable-imitation-policy-with-waypoints
2024-10-01
ArXiv (prépublication)
doi.org
arxiv.org
A Survey of Diversification Techniques in Search and Recommendation
Haolun Wu
Yansen Zhang
Chen Ma
Fuyuan Lyu
Bowei He
Fernando Diaz
Bhaskar Mitra
Xue (Steve) Liu
Diversifying search results is an important research topic in retrieval systems in order to satisfy both the various interests of customers … (voir plus)and the equal market exposure of providers. There has been a growing attention on diversity-aware research during recent years, accompanied by a proliferation of literature on methods to promote diversity in search and recommendation. However, the diversity-aware studies in retrieval systems lack a systematic organization and are rather fragmented. In this survey, we are the first to propose a unified taxonomy for classifying the metrics and approaches of diversification in both search and recommendation, which are two of the most extensively researched fields of retrieval systems. We begin the survey with a brief discussion of why diversity is important in retrieval systems, followed by a summary of the various diversity concerns in search and recommendation, highlighting their relationship and differences. For the survey’s main body, we present a unified taxonomy of diversification metrics and approaches in retrieval systems, from both the search and recommendation perspectives. In the later part of the survey, we discuss the openness research questions of diversity-aware research in search and recommendation in an effort to inspire future innovations and encourage the implementation of diversity in real-world systems.
2024-10-01
IEEE Transactions on Knowledge and Data Engineering (publié)
doi.org
arxiv.org
Towards a unified XAI-based framework for digital forensic investigations
Zainab Khalid
Farkhund Iqbal
Benjamin Fung
2024-10-01
Forensic Science International: Digital Investigation (publié)
doi.org
The oneirogen hypothesis: modeling the hallucinatory effects of classical psychedelics in terms of replay-dependent plasticity mechanisms
Colin Bredenberg
Fabrice Normandin
Blake Richards
Guillaume Lajoie
2024-09-30
bioRxiv (prépublication)
doi.org
What Information Contributes to Log-based Anomaly Detection? Insights from a Configurable Transformer-Based Approach
Xingfang Wu
Heng Li
Foutse Khomh
Log data are generated from logging statements in the source code, providing insights into the execution processes of software applications … (voir plus)and systems. State-of-the-art log-based anomaly detection approaches typically leverage deep learning models to capture the semantic or sequential information in the log data and detect anomalous runtime behaviors. However, the impacts of these different types of information are not clear. In addition, existing approaches have not captured the timestamps in the log data, which can potentially provide more fine-grained temporal information than sequential information. In this work, we propose a configurable transformer-based anomaly detection model that can capture the semantic, sequential, and temporal information in the log data and allows us to configure the different types of information as the model's features. Additionally, we train and evaluate the proposed model using log sequences of different lengths, thus overcoming the constraint of existing methods that rely on fixed-length or time-windowed log sequences as inputs. With the proposed model, we conduct a series of experiments with different combinations of input features to evaluate the roles of different types of information in anomaly detection. When presented with log sequences of varying lengths, the model can attain competitive and consistently stable performance compared to the baselines. The results indicate that the event occurrence information plays a key role in identifying anomalies, while the impact of the sequential and temporal information is not significant for anomaly detection in the studied public datasets. On the other hand, the findings also reveal the simplicity of the studied public datasets and highlight the importance of constructing new datasets that contain different types of anomalies to better evaluate the performance of anomaly detection models.
2024-09-30
ArXiv (prépublication)
doi.org
arxiv.org
Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision
Mélanie Gaillochet
Christian Desrosiers
Hervé Lombaert
2024-09-28
Lecture Notes in Computer Science (publié)
doi.org
arxiv.org
Linear Weight Interpolation Leads to Transient Performance Gains
Gaurav Iyer
Gintare Karolina Dziugaite
David Rolnick
2024-09-28
TMLR (accepté)
openreview.net
openreview.net
CALE: Continuous Arcade Learning Environment
Jesse Farebrother
Pablo Samuel Castro
We introduce the Continuous Arcade Learning Environment (CALE), an extension of the well-known Arcade Learning Environment (ALE) [Bellemare … (voir plus)et al., 2013]. The CALE uses the same underlying emulator of the Atari 2600 gaming system (Stella), but adds support for continuous actions. This enables the benchmarking and evaluation of continuous-control agents (such as PPO [Schulman et al., 2017] and SAC [Haarnoja et al., 2018]) and value-based agents (such as DQN [Mnih et al., 2015] and Rainbow [Hessel et al., 2018]) on the same environment suite. We provide a series of open questions and research directions that CALE enables, as well as initial baseline results using Soft Actor-Critic. CALE is available as part of the ALE athttps://github.com/Farama-Foundation/Arcade-Learning-Environment.
2024-09-26
NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (poster)
openreview.net
openreview.net
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
David Orlando Romero Mogrovejo
Chenyang Lyu
Haryo Akbarianto Wibowo
Santiago Góngora
Aishik Mandal
Sukannya Purkayastha
Jesus-German Ortiz-Barajas
Emilio Villa Cueva
Jinheon Baek
Soyeong Jeong
Injy Hamed
Zheng Xin Yong
Zheng Wei Lim
Paula Mónica Silva
Jocelyn Dunstan
D. Meur
Mélanie Jouitteau
David LE MEUR
Joan Nwatu
Ganzorig Batnasan … (voir 57 de plus)
Munkh-Erdene Otgonbold
Munkhjargal Gochoo
Guido Ivetta
Luciana Benotti
Laura Alonso Alemany
Hernán Maina
Jiahui Geng
Tiago Timponi Torrent
Frederico Belcavello
Marcelo Viridiano
Jan Christian Blaise Cruz
Dan John Velasco
Oana Ignat
Zara Burzo
Chenxi Whitehouse
Artem Abzaliev
Teresa Clifford
Gráinne Caulfield
Teresa Lynn
Christian Salamea-Palacios
Vladimir Araujo
Yova Kementchedjhieva
Mihail Minkov Mihaylov
Israel Abebe Azime
Henok Biadglign Ademtew
Bontu Fufa Balcha
Naome Etori
David Ifeoluwa Adelani
Rada Mihalcea
Atnafu Lambebo Tonja
Maria Camila Buitrago Cabrera
Gisela Vallejo
Holy Lovenia
Ruochen Zhang
Marcos Estecha-Garitagoitia
Mario Rodríguez-Cantelar
Toqeer Ehsan
Rendi Chevi
Muhammad Farid Adilazuarda
Ryandito Diandaru
Samuel Cahyawijaya
Fajri Koto
Tatsuki Kuribayashi
Haiyue Song
Aditya Nanda Kishore Khandavally
Thanmay Jayakumar
Raj Dabre
Mohamed Fazli Mohamed Imam
Kumaranage Ravindu Yasas Nagasinghe
Alina Dragonetti
Luis Fernando D'Haro
Olivier NIYOMUGISHA
Jay Gala
Pranjal A Chitale
Fauzan Farooqui
Thamar Solorio
Alham Fikri Aji
2024-09-26
NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (présentation orale)
doi.org
openreview.net
Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection
Charles Guille-Escuret
Pierre-Andre Noel
Ioannis Mitliagkas
David Vazquez
Joao Monteiro
2024-09-26
NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (poster)
doi.org
openreview.net
Learning Action and Reasoning-Centric Image Editing from Videos and Simulation
Benno Krojer
Dheeraj Vattikonda
Luis Lara
Varun Jampani
Eva Portelance
Chris Pal
Siva Reddy
2024-09-26
NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (spotlight)
openreview.net
openreview.net