Publications

A Hybrid CNN-Transformer Approach for Continuous Fine Finger Motion Decoding from sEMG Signals
Zihan Weng
Xiabing Zhang
Yufeng Mou
Chanlin Yi
Fali Li
Pouya Bashivan
Peng Xu
This work presents a novel approach that synergistically integrates convolutional neural networks (CNNs) and Transformer models for decoding… (see more) continuous fine finger motions from surface electromyography (sEMG) signals. This integration capitalizes on CNNs’ proficiency in extracting rich temporal and spatial features from multichannel sEMG data and the Transformer’s superior capability in recognizing complex patterns and long-range dependencies. A significant advancement in this field is the use of a custom-developed Epidermal Electrode Array Sleeve (EEAS) for capturing high-fidelity sEMG signals, enabling more accurate and reliable signal acquisition than traditional methods. The decoded joint angles could be used in seamless and intuitive human-machine interaction in various applications, such as virtual reality, augmented reality, robotic control, and prosthetic control. Evaluations demonstrate the superior performance of the proposed CNN-Transformer hybrid architecture in decoding continuous fine finger motions, outperforming individual CNN and Transformer models. The synergistic integration of CNNs and Transformers presents a powerful framework for sEMG decoding, offering exciting opportunities for naturalistic and intuitive human-machine interaction applications. Its robustness and efficiency make it an ideal choice for real-world applications, promising to enhance the interface between humans and machines significantly. The implications of this research extend to advancing the understanding of human neuromuscular signals and their application in computing interfaces.
2024-06-14
2024 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) (published)
doi.org
Phoneme Discretized Saliency Maps for Explainable Detection of AI-Generated Voice
Shubham Gupta
Mirco Ravanelli
Pascal Germain
Cem (Yusuf) Subakan
2024-06-14
ArXiv (preprint)
doi.org
arxiv.org
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
Julia Gastinger
Shenyang Huang
Mikhail Galkin
Erfan Loghmani
Ali Parviz
Farimah Poursafaei
Jacob Danovitch
Emanuele Rossi
Ioannis Koutis
Heiner Stuckenschmidt
Reihaneh Rabbany
Guillaume Rabusseau
2024-06-14
ArXiv (preprint)
doi.org
arxiv.org
Towards Neural Scaling Laws for Foundation Models on Temporal Graphs
Razieh Shirzadkhani
Tran Gia Bao Ngo
Kiarash Shamsi
Shenyang Huang
Farimah Poursafaei
Poupak Azad
Reihaneh Rabbany
Baris Coskunuzer
Guillaume Rabusseau
Cuneyt Gurcan Akcora
The field of temporal graph learning aims to learn from evolving network data to forecast future interactions. Given a collection of observe… (see more)d temporal graphs, is it possible to predict the evolution of an unseen network from the same domain? To answer this question, we first present the Temporal Graph Scaling (TGS) dataset, a large collection of temporal graphs consisting of eighty-four ERC20 token transaction networks collected from 2017 to 2023. Next, we evaluate the transferability of Temporal Graph Neural Networks (TGNNs) for the temporal graph property prediction task by pre-training on a collection of up to sixty-four token transaction networks and then evaluating the downstream performance on twenty unseen token networks. We find that the neural scaling law observed in NLP and Computer Vision also applies in temporal graph learning, where pre-training on greater number of networks leads to improved downstream performance. To the best of our knowledge, this is the first empirical demonstration of the transferability of temporal graphs learning. On downstream token networks, the largest pre-trained model outperforms single model TGNNs on thirteen unseen test networks. Therefore, we believe that this is a promising first step towards building foundation models for temporal graphs.
2024-06-14
ArXiv (preprint)
doi.org
arxiv.org
Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition
Eleni Triantafillou
Peter Kairouz
Fabian Pedregosa
Jamie Hayes
Meghdad Kurmanji
Kairan Zhao
Vincent Dumoulin
Julio C. S. Jacques Junior
Ioannis Mitliagkas
Jun Wan
Lisheng Sun-Hosoya
Sergio Escalera
Gintare Karolina Dziugaite
Peter Triantafillou
Isabelle Guyon
We present the findings of the first NeurIPS competition on unlearning, which sought to stimulate the development of novel algorithms and in… (see more)itiate discussions on formal and robust evaluation methodologies. The competition was highly successful: nearly 1,200 teams from across the world participated, and a wealth of novel, imaginative solutions with different characteristics were contributed. In this paper, we analyze top solutions and delve into discussions on benchmarking unlearning, which itself is a research problem. The evaluation methodology we developed for the competition measures forgetting quality according to a formal notion of unlearning, while incorporating model utility for a holistic evaluation. We analyze the effectiveness of different instantiations of this evaluation framework vis-a-vis the associated compute cost, and discuss implications for standardizing evaluation. We find that the ranking of leading methods remains stable under several variations of this framework, pointing to avenues for reducing the cost of evaluation. Overall, our findings indicate progress in unlearning, with top-performing competition entries surpassing existing algorithms under our evaluation framework. We analyze trade-offs made by different algorithms and strengths or weaknesses in terms of generalizability to new datasets, paving the way for advancing both benchmarking and algorithm development in this important area.
2024-06-13
ArXiv (preprint)
doi.org
arxiv.org
Exploring validation metrics for offline model-based optimisation with diffusion models
Christopher Beckham
Alexandre Piché
David Vazquez
Chris Pal
2024-06-13
TMLR (accepted)
openreview.net
openreview.net
GIST: Generated Inputs Sets Transferability in Deep Learning
Florian Tambon
Foutse Khomh
Giuliano Antoniol
2024-06-13
ACM Transactions on Software Engineering and Methodology (published)
doi.org
arxiv.org
Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving
Aniket Rajiv Didolkar
Anirudh Goyal
Nan Rosemary Ke
Siyuan Guo
Michal Valko
Timothy P Lillicrap
Danilo Jimenez Rezende
Yoshua Bengio
Michael Curtis Mozer
Sanjeev Arora
2024-06-13
ICML.cc/2024/Workshop/AI4MATH (poster)
doi.org
openreview.net
Turns Out I'm Not Real: Towards Robust Detection of AI-Generated Videos
Qingyuan Liu
Pengyuan Shi
Yun-Yun Tsai
Chengzhi Mao
Junfeng Yang
2024-06-13
ArXiv (preprint)
doi.org
arxiv.org
Grounding Multimodal Large Language Models in Actions
Andrew Szot
Bogdan Mazoure
Harsh Agrawal
(Rex) Devon Hjelm
Zsolt Kira
Alexander T Toshev
2024-06-12
ArXiv (preprint)
doi.org
arxiv.org
PathOCL: Path-Based Prompt Augmentation for OCL Generation with GPT-4
Seif Abukhalaf
Mohammad Hamdaqa
Foutse Khomh
The rapid progress of AI-powered programming assistants, such as GitHub Copilot, has facilitated the development of software applications. T… (see more)hese assistants rely on large language models (LLMs), which are foundation models (FMs) that support a wide range of tasks related to understanding and generating language. LLMs have demonstrated their ability to express UML model specifications using formal languages like the Object Constraint Language (OCL). However, the context size of the prompt is limited by the number of tokens an LLM can process. This limitation becomes significant as the size of UML class models increases. In this study, we introduce PathOCL, a novel path-based prompt augmentation technique designed to facilitate OCL generation. PathOCL addresses the limitations of LLMs, specifically their token processing limit and the challenges posed by large UML class models. PathOCL is based on the concept of chunking, which selectively augments the prompts with a subset of UML classes relevant to the English specification. Our findings demonstrate that PathOCL, compared to augmenting the complete UML class model (UML-Augmentation), generates a higher number of valid and correct OCL constraints using the GPT-4 model. Moreover, the average prompt size crafted using PathOCL significantly decreases when scaling the size of the UML class models.
2024-06-12
Proceedings of the 2024 IEEE/ACM First International Conference on AI Foundation Models and Software Engineering (published)
doi.org
arxiv.org
Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences
Damien Ferbach
Quentin Bertrand
Joey Bose
Gauthier Gidel
The rapid progress in generative models has resulted in impressive leaps in generation quality, blurring the lines between synthetic and rea… (see more)l data. Web-scale datasets are now prone to the inevitable contamination by synthetic data, directly impacting the training of future generated models. Already, some theoretical results on self-consuming generative models (a.k.a., iterative retraining) have emerged in the literature, showcasing that either model collapse or stability could be possible depending on the fraction of generated data used at each retraining step. However, in practice, synthetic data is often subject to human feedback and curated by users before being used and uploaded online. For instance, many interfaces of popular text-to-image generative models, such as Stable Diffusion or Midjourney, produce several variations of an image for a given query which can eventually be curated by the users. In this paper, we theoretically study the impact of data curation on iterated retraining of generative models and show that it can be seen as an \emph{implicit preference optimization mechanism}. However, unlike standard preference optimization, the generative model does not have access to the reward function or negative samples needed for pairwise comparisons. Moreover, our study doesn't require access to the density function, only to samples. We prove that, if the data is curated according to a reward model, then the expected reward of the iterative retraining procedure is maximized. We further provide theoretical results on the stability of the retraining loop when using a positive fraction of real data at each step. Finally, we conduct illustrative experiments on both synthetic datasets and on CIFAR10 showing that such a procedure amplifies biases of the reward model.
2024-06-12
ArXiv (preprint)
doi.org
arxiv.org