Search Results for author:
Found 15 papers, 8 papers with code
LOCOST: State-Space Models for Long Document Abstractive Summarization
State-space models are a low-complexity alternative to transformers for encoding long sequences and capturing long-term dependencies.
Navigating Uncertainty: Optimizing API Dependency for Hallucination Reduction in Closed-Book Question Answering
Our model directly provides answers for $78. 2\%$ of the known queries and opts to search for $77. 2\%$ of the unknown ones.
Interpretable time series neural representation for classification purposes
In this work, we propose a set of requirements for a neural representation of univariate time series to be interpretable.
Improving generalization in large language models by learning prefix subspaces
We show in this paper that "Parameter Efficient Fine-Tuning" (PEFT) methods, however, are perfectly compatible with this original approach, and propose to learn entire simplex of continuous prefixes.
Of Spiky SVDs and Music Recommendation
The truncated singular value decomposition is a widely used methodology in music recommendation for direct similar-item retrieval or embedding musical items for downstream tasks.
Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations
We introduce a novel modeling approach for time series imputation and forecasting, tailored to address the challenges often encountered in real-world data, such as irregular samples, missing data, or unaligned measurements from multiple sensors.
Dynamic Named Entity Recognition
Named Entity Recognition (NER) is a challenging and widely studied task that involves detecting and typing entities in text.
Learning Unsupervised Hierarchies of Audio Concepts
In this paper, we adapt concept learning to the realm of music, with its particularities.
Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction
State-of-the-art NLP models can adopt shallow heuristics that limit their generalization capability (McCoy et al., 2019).
Towards Rigorous Interpretations: a Formalisation of Feature Attribution
Feature attribution is often loosely presented as the process of selecting a subset of relevant features as a rationale of a prediction.
Let's Stop Incorrect Comparisons in End-to-end Relation Extraction!
Despite efforts to distinguish three different evaluation setups (Bekoulis et al., 2018), numerous end-to-end Relation Extraction (RE) articles present unreliable performance comparison to previous work.
Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on Generalization
Contextualized embeddings use unsupervised language model pretraining to compute word representations depending on their context.
Unsupervised Information Extraction: Regularizing Discriminative Approaches with Relation Distribution Losses
Unsupervised relation extraction aims at extracting relations between entities in text.
DEFT 2018: Attention s\'elective pour classification de microblogs (DEFT 2018 : Selective Attention for Microblogging Classification )
Dans le cadre de l{'}atelier DEFT 2018 nous nous sommes int{\'e}ress{\'e}s {\`a} la classification de microblogs (ici, des tweets) r{\'e}dig{\'e}s en fran{\c{c}}ais.
Extended Recommendation Framework: Generating the Text of a User Review as a Personalized Summary
We propose to augment rating based recommender systems by providing the user with additional information which might help him in his choice or in the understanding of the recommendation.
