Publications

Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models
Pierre Colombo
Victor Pellegrain
Malik Boudiaf
Victor Storchan
Myriam Tami
Ismail Ben Ayed
C'eline Hudelot
Pablo Piantanida
Proprietary and closed APIs are becoming increasingly common to process natural language, and are impacting the practical applications of na… (voir plus)tural language processing, including few-shot classification. Few-shot classification involves training a model to perform a new classification task with a handful of labeled data. This paper presents three contributions. First, we introduce a scenario where the embedding of a pre-trained model is served through a gated API with compute-cost and data-privacy constraints. Second, we propose a transductive inference, a learning paradigm that has been overlooked by the NLP community. Transductive inference, unlike traditional inductive learning, leverages the statistics of unlabeled data. We also introduce a new parameter-free transductive regularizer based on the Fisher-Rao loss, which can be used on top of the gated API embeddings. This method fully utilizes unlabeled data, does not share any label with the third-party API provider and could serve as a baseline for future research. Third, we propose an improved experimental setting and compile a benchmark of eight datasets involving multiclass classification in four different languages, with up to 151 classes. We evaluate our methods using eight backbone models, along with an episodic evaluation over 1,000 episodes, which demonstrate the superiority of transductive inference over the standard inductive setting.
2023-10-07
EMNLP/2023/Conference (accepté)
doi.org
openreview.net
Using In-Context Learning to Improve Dialogue Safety
Nicholas Meade
Spandana Gella
Devamanyu Hazarika
Prakhar Gupta
Di Jin
Siva Reddy
Yang Liu
Dilek Hakkani-Tur
2023-10-07
EMNLP/2023/Conference (publié)
doi.org
openreview.net
DragD3D: Vertex-based Editing for Realistic Mesh Deformations using 2D Diffusion Priors
Tianhao Xie
Eugene Belilovsky
Sudhir Mudur
Tiberiu Popa
Direct mesh editing and deformation are key components in the geometric modeling and animation pipeline. Direct mesh editing methods are typ… (voir plus)ically framed as optimization problems combining user-specified vertex constraints with a regularizer that determines the position of the rest of the vertices. The choice of the regularizer is key to the realism and authenticity of the final result. Physics and geometry-based regularizers are not aware of the global context and semantics of the object, and the more recent deep learning priors are limited to a specific class of 3D object deformations. In this work, our main contribution is a local mesh editing method called DragD3D for global context-aware realistic deformation through direct manipulation of a few vertices. DragD3D is not restricted to any class of objects. It achieves this by combining the classic geometric ARAP (as rigid as possible) regularizer with 2D priors obtained from a large-scale diffusion model. Specifically, we render the objects from multiple viewpoints through a differentiable renderer and use the recently introduced DDS loss which scores the faithfulness of the rendered image to one from a diffusion model. DragD3D combines the approximate gradients of the DDS with gradients from the ARAP loss to modify the mesh vertices via neural Jacobian field, while also satisfying vertex constraints. We show that our deformations are realistic and aware of the global context of the objects, and provide better results than just using geometric regularizers.
2023-10-06
ArXiv (prépublication)
doi.org
arxiv.org
Evolution of High Throughput Satellite Systems: Vision, Requirements, and Key Technologies
Olfa Ben Yahia
Zineb Garroussi
Olivier B'elanger
Brunilde Sansò
J. Frigon
St'ephane Martel
Antoine Lesage-Landry
G. Kurt
High throughput satellites (HTS), with their digital payload technology, are expected to play a key role as enablers of the upcoming 6G netw… (voir plus)orks. HTS are mainly designed to provide higher data rates and capacities. Fueled by technological advancements including beamforming, advanced modulation techniques, reconfigurable phased array technologies, and electronically steerable antennas, HTS have emerged as a fundamental component for future network generation. This paper offers a comprehensive state-of-the-art of HTS systems, with a focus on standardization, patents, channel multiple access techniques, routing, load balancing, and the role of software-defined networking (SDN). In addition, we provide a vision for next-satellite systems that we named as extremely-HTS (EHTS) toward autonomous satellites supported by the main requirements and key technologies expected for these systems. The EHTS system will be designed such that it maximizes spectrum reuse and data rates, and flexibly steers the capacity to satisfy user demand. We introduce a novel architecture for future regenerative payloads while summarizing the challenges imposed by this architecture.
2023-10-06
ArXiv (prépublication)
arxiv.org
arxiv.org
Realizing XR Applications Using 5G-Based 3D Holographic Communication and Mobile Edge Computing
Dun Yuan
Ekram Hossain
Di Wu
Xue (Steve) Liu
Gregory Dudek
3D holographic communication has the potential to revolutionize the way people interact with each other in virtual spaces, offering immersiv… (voir plus)e and realistic experiences. However, demands for high data rates, extremely low latency, and high computations to enable this technology pose a significant challenge. To address this challenge, we propose a novel job scheduling algorithm that leverages Mobile Edge Computing (MEC) servers in order to minimize the total latency in 3D holographic communication. One of the motivations for this work is to prevent the uncanny valley effect, which can occur when the latency hinders the seamless and real-time rendering of holographic content, leading to a less convincing and less engaging user experience. Our proposed algorithm dynamically allocates computation tasks to MEC servers, considering the network conditions, computational capabilities of the servers, and the requirements of the 3D holographic communication application. We conduct extensive experiments to evaluate the performance of our algorithm in terms of latency reduction, and the results demonstrate that our approach significantly outperforms other baseline methods. Furthermore, we present a practical scenario involving Augmented Reality (AR), which not only illustrates the applicability of our algorithm but also highlights the importance of minimizing latency in achieving high-quality holographic views. By efficiently distributing the computation workload among MEC servers and reducing the overall latency, our proposed algorithm enhances the user experience in 3D holographic communications and paves the way for the widespread adoption of this technology in various applications, such as telemedicine, remote collaboration, and entertainment.
2023-10-06
ArXiv (prépublication)
doi.org
arxiv.org
Adaptive Dynamic Programming for Energy-Efficient Base Station Cell Switching
Junliang Luo
Yi Tian Xu
Di Wu
M. Jenkin
Xue (Steve) Liu
Gregory Dudek
Energy saving in wireless networks is growing in importance due to increasing demand for evolving new-gen cellular networks, environmental a… (voir plus)nd regulatory concerns, and potential energy crises arising from geopolitical tensions. In this work, we propose an approximate dynamic programming (ADP)-based method coupled with online optimization to switch on/off the cells of base stations to reduce network power consumption while maintaining adequate Quality of Service (QoS) metrics. We use a multilayer perceptron (MLP) given each state-action pair to predict the power consumption to approximate the value function in ADP for selecting the action with optimal expected power saved. To save the largest possible power consumption without deteriorating QoS, we include another MLP to predict QoS and a long short-term memory (LSTM) for predicting handovers, incorporated into an online optimization algorithm producing an adaptive QoS threshold for filtering cell switching actions based on the overall QoS history. The performance of the method is evaluated using a practical network simulator with various real-world scenarios with dynamic traffic patterns.
2023-10-05
ArXiv (prépublication)
doi.org
arxiv.org
Causal Inference in Gene Regulatory Networks with GFlowNet: Towards Scalability in Large Systems
Trang Nguyen
Alexander Tong
Kanika Madan
Yoshua Bengio
Dianbo Liu
Understanding causal relationships within Gene Regulatory Networks (GRNs) is essential for unraveling the gene interactions in cellular proc… (voir plus)esses. However, causal discovery in GRNs is a challenging problem for multiple reasons including the existence of cyclic feedback loops and uncertainty that yields diverse possible causal structures. Previous works in this area either ignore cyclic dynamics (assume acyclic structure) or struggle with scalability. We introduce Swift-DynGFN as a novel framework that enhances causal structure learning in GRNs while addressing scalability concerns. Specifically, Swift-DynGFN exploits gene-wise independence to boost parallelization and to lower computational cost. Experiments on real single-cell RNA velocity and synthetic GRN datasets showcase the advancement in learning causal structure in GRNs and scalability in larger systems.
2023-10-05
ArXiv (prépublication)
doi.org
arxiv.org
Improved baselines for vision-language pre-training
Enrico Fini
Pietro Astolfi
Adriana Romero Soriano
Jakob Verbeek
Michal Drozdzal
2023-10-05
TMLR (accepté)
doi.org
openreview.net
Local Search GFlowNets
Minsu Kim
Taeyoung Yun
Emmanuel Bengio
Dinghuai Zhang
Yoshua Bengio
Sungsoo Ahn
Jinkyoo Park
Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their re… (voir plus)wards. GFlowNets exhibit a remarkable ability to generate diverse samples, yet occasionally struggle to consistently produce samples with high rewards due to over-exploration on wide sample space. This paper proposes to train GFlowNets with local search, which focuses on exploiting high-rewarded sample space to resolve this issue. Our main idea is to explore the local neighborhood via backtracking and reconstruction guided by backward and forward policies, respectively. This allows biasing the samples toward high-reward solutions, which is not possible for a typical GFlowNet solution generation scheme, which uses the forward policy to generate the solution from scratch. Extensive experiments demonstrate a remarkable performance improvement in several biochemical tasks. Source code is available: https://github.com/dbsxodud-11/ls_gfn.
2023-10-04
ArXiv (prépublication)
doi.org
arxiv.org
Searching for High-Value Molecules Using Reinforcement Learning and Transformers
Raj Ghugare
Santiago Miret
Adriana Hugessen
Mariano Phielipp
Glen Berseth
Reinforcement learning (RL) over text representations can be effective for finding high-value policies that can search over graphs. However,… (voir plus) RL requires careful structuring of the search space and algorithm design to be effective in this challenge. Through extensive experiments, we explore how different design choices for text grammar and algorithmic choices for training can affect an RL policy's ability to generate molecules with desired properties. We arrive at a new RL-based molecular design algorithm (ChemRLformer) and perform a thorough analysis using 25 molecule design tasks, including computationally complex protein docking simulations. From this analysis, we discover unique insights in this problem space and show that ChemRLformer achieves state-of-the-art performance while being more straightforward than prior work by demystifying which design choices are actually helpful for text-based molecule design.
2023-10-04
ArXiv (prépublication)
doi.org
arxiv.org
Sensing Wellbeing in the Workplace, Why and For Whom? Envisioning Impacts with Organizational Stakeholders
Anna Kawakami
Shreya Chowdhary
Shamsi T. Iqbal
Q. Vera Liao
Alexandra Olteanu
Jina Suh
Koustuv Saha
With the heightened digitization of the workplace, alongside the rise of remote and hybrid work prompted by the pandemic, there is growing c… (voir plus)orporate interest in using passive sensing technologies for workplace wellbeing. Existing research on these technologies often focus on understanding or improving interactions between an individual user and the technology. Workplace settings can, however, introduce a range of complexities that challenge the potential impact and in-practice desirability of wellbeing sensing technologies. Today, there is an inadequate empirical understanding of how everyday workers---including those who are impacted by, and impact the deployment of workplace technologies--envision its broader socio-ecological impacts. In this study, we conduct storyboard-driven interviews with 33 participants across three stakeholder groups: organizational governors, AI builders, and worker data subjects. Overall, our findings surface how workers envisioned wellbeing sensing technologies may lead to cascading impacts on their broader organizational culture, interpersonal relationships with colleagues, and individual day-to-day lives. Participants anticipated harms arising from ambiguity and misalignment around scaled notions of "worker wellbeing,'' underlying technical limitations to workplace-situated sensing, and assumptions regarding how social structures and relationships may shape the impacts and use of these technologies. Based on our findings, we discuss implications for designing worker-centered data-driven wellbeing technologies.
2023-10-04
Proceedings of the ACM on Human-Computer Interaction (publié)
doi.org
arxiv.org
SUMMIT: Scaffolding Open Source Software Issue Discussion Through Summarization
Saskia Gilmer
Avinash Bhat
Shuvam Shah
Kevin Cherry
Jinghui Cheng
Jin Guo
2023-10-04
Proceedings of the ACM on Human-Computer Interaction (publié)
doi.org
arxiv.org