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Found 34 papers, 20 papers with code
Choisir le bon co-équipier pour la génération coopérative de texte (Choosing The Right Teammate For Cooperative Text Generation)
Nous évaluons leurs avantages et inconvénients, en explorant leur précision respective sur des tâches de classification, ainsi que leur impact sur la génération coopérative et leur coût de calcul, dans le cadre d’une stratégie de décodage état de l’art, basée sur une recherche arborescente de Monte-Carlo (MCTS).
A RECURRENT NEURAL CASCADE-BASED MODEL FOR CONTINUOUS-TIME DIFFUSION PROCESS
Many works have been proposed in the literature to capture the dynamics of diffusion in networks.
Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey
Deep Reinforcement Learning (DRL) is an approach for training autonomous agents across various complex environments.
Training Table Question Answering via SQL Query Decomposition
Table Question-Answering involves both understanding the natural language query and grounding it in the context of the input table to extract the relevant information.
Deinterleaving of Discrete Renewal Process Mixtures with Application to Electronic Support Measures
In this paper, we propose a new deinterleaving method for mixtures of discrete renewal Markov chains.
On the Fairness ROAD: Robust Optimization for Adversarial Debiasing
In the field of algorithmic fairness, significant attention has been put on group fairness criteria, such as Demographic Parity and Equalized Odds.
Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search
Symbolic regression (SR) is the problem of learning a symbolic expression from numerical data.
Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning
Using an interactive textual environment designed to study higher-level forms of functional grounding, and a set of spatial and navigation tasks, we study several scientific questions: 1) Can LLMs boost sample efficiency for online learning of various RL tasks?
EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RL
Reinforcement learning (RL) in long horizon and sparse reward tasks is notoriously difficult and requires a lot of training steps.
Stein Variational Goal Generation for adaptive Exploration in Multi-Goal Reinforcement Learning
In multi-goal Reinforcement Learning, an agent can share experience between related training tasks, resulting in better generalization for new tasks at test time.
Which Discriminator for Cooperative Text Generation?
Language models generate texts by successively predicting probability distributions for next tokens given past ones.
Generative Cooperative Networks for Natural Language Generation
Generative Adversarial Networks (GANs) have known a tremendous success for many continuous generation tasks, especially in the field of image generation.
Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching
Learning meaningful behaviors in the absence of reward is a difficult problem in reinforcement learning.
Fairness without the sensitive attribute via Causal Variational Autoencoder
In recent years, most fairness strategies in machine learning models focus on mitigating unwanted biases by assuming that the sensitive information is observed.
To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs
Due to the discrete nature of words, language GANs require to be optimized from rewards provided by discriminator networks, via reinforcement learning methods.
A Neural Tangent Kernel Perspective of GANs
We propose a novel theoretical framework of analysis for Generative Adversarial Networks (GANs).
Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation
QuestEval is a reference-less metric used in text-to-text tasks, that compares the generated summaries directly to the source text, by automatically asking and answering questions.
Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic Network
Existing approaches of the literature to generate meaningful disturbances of the environment are adversarial reinforcement learning methods.
QuestEval: Summarization Asks for Fact-based Evaluation
Summarization evaluation remains an open research problem: current metrics such as ROUGE are known to be limited and to correlate poorly with human judgments.
Stochastic sparse adversarial attacks
This paper introduces stochastic sparse adversarial attacks (SSAA), standing as simple, fast and purely noise-based targeted and untargeted attacks of neural network classifiers (NNC).
Learning Unbiased Representations via Rényi Minimization
We leverage recent work which has been done to estimate this coefficient by learning deep neural network transformations and use it as a minmax game to penalize the intrinsic bias in a multi dimensional latent representation.
Adversarial Learning for Counterfactual Fairness
In recent years, fairness has become an important topic in the machine learning research community.
PDE-Driven Spatiotemporal Disentanglement
A recent line of work in the machine learning community addresses the problem of predicting high-dimensional spatiotemporal phenomena by leveraging specific tools from the differential equations theory.
ColdGANs: Taming Language GANs with Cautious Sampling Strategies
Training regimes based on Maximum Likelihood Estimation (MLE) suffer from known limitations, often leading to poorly generated text sequences.
MLSUM: The Multilingual Summarization Corpus
We present MLSUM, the first large-scale MultiLingual SUMmarization dataset.
Discriminative Adversarial Search for Abstractive Summarization
We introduce a novel approach for sequence decoding, Discriminative Adversarial Search (DAS), which has the desirable properties of alleviating the effects of exposure bias without requiring external metrics.
Stochastic Latent Residual Video Prediction
Designing video prediction models that account for the inherent uncertainty of the future is challenging.
Ranked #1 on
Video Prediction
on Cityscapes 128x128
(Pred metric)
Fairness-Aware Neural Réyni Minimization for Continuous Features
Second, by minimizing the HGR directly with an adversarial neural network architecture.
Learning Dynamic Author Representations with Temporal Language Models
By conditioning language models with author and temporal vector states, we are able to leverage the latent dependencies between the text contexts.
Answers Unite! Unsupervised Metrics for Reinforced Summarization Models
Abstractive summarization approaches based on Reinforcement Learning (RL) have recently been proposed to overcome classical likelihood maximization.
A Variational Topological Neural Model for Cascade-based Diffusion in Networks
Many works have been proposed in the literature to capture the dynamics of diffusion in networks.
Parameterized Neural Network Language Models for Information Retrieval
Although they present good abilities to cope with both term dependencies and vocabulary mismatch problems, thanks to the distributed representation of words they are based upon, such models could not be used readily in IR, where the estimation of one language model per document (or query) is required.
Learning Information Spread in Content Networks
We introduce a model for predicting the diffusion of content information on social media.
