Le traitement du langage naturel à l'ère de l'IA générative
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Publications
Learning Lagrangian Multipliers for the Travelling Salesman Problem
Neuronal network activity is thought to be structured around the activation of assemblies, or low-dimensional manifolds describing states of… (voir plus) activity. Both views describe neurons acting not independently, but in concert, likely facilitated by strong recurrent excitation between them. The role of inhibition in these frameworks – if considered at all – is often reduced to blanket inhibition with no specificity with respect to which excitatory neurons are targeted. We analyzed the structure of excitation and inhibition in the MICrONS 1mm3 dataset, an electron microscopic reconstruction of a piece of cortical tissue. We found that excitation was structured around a feed-forward flow in non-random motifs of seven or more neurons. This revealed a structure of information flow from a small number of sources to a larger number of potential targets that became only visible when larger motifs were considered instead of individual pairs. Inhibitory neurons targeted and were targeted by neurons in specific sequential positions of these motifs. Additionally, disynaptic inhibition was strongest between target motifs excited by the same group of source neurons, implying competition between them. The structure of this inhibition was also highly specific and symmetrical, contradicting the idea of non-specific blanket inhibition. None of these trends are detectable in only pairwise connectivity, demonstrating that inhibition is specifically structured by these large motifs. Further, we found that these motifs represent higher order connectivity patterns which are present, but to a lesser extent in a recently released, detailed computational model, and not at all in a distance-dependent control. These findings have important implications for how synaptic plasticity reorganizes neocortical connectivity to implement learning and for the specific role of inhibition in this process.
The extraction of a small number of relevant insights from vast amounts of data is a crucial component of data-driven decision-making. Howev… (voir plus)er, accomplishing this task requires considerable technical skills, domain expertise, and human labor. This study explores the potential of using Large Language Models (LLMs) to automate the discovery of insights in data, leveraging recent advances in reasoning and code generation techniques. We propose a new evaluation methodology based on a"capture the flag"principle, measuring the ability of such models to recognize meaningful and pertinent information (flags) in a dataset. We further propose two proof-of-concept agents, with different inner workings, and compare their ability to capture such flags in a real-world sales dataset. While the work reported here is preliminary, our results are sufficiently interesting to mandate future exploration by the community.
Deep spectroscopic surveys with the Atacama Large Millimeter/submillimeter Array (ALMA) have revealed that some of the brightest infrared so… (voir plus)urces in the sky correspond to concentrations of submillimeter galaxies (SMGs) at high redshift. Among these, the SPT2349-56 protocluster system is amongst the most extreme examples given its high source density and integrated star formation rate. We conducted a deep Lyman-alpha line emission survey around SPT2349-56 using the Multi-Unit Spectroscopic Explorer (MUSE) at the Very Large Telescope (VLT) in order to characterize this uniquely dense environment. Taking advantage of the deep three-dimensional nature of this survey, we performed a sensitive search for Lyman-alpha emitters (LAEs) toward the core and northern extension of the protocluster, which correspond to the brightest infrared regions in this field. Using a smoothed narrowband image extracted from the MUSE datacube around the protocluster redshift, we searched for possible extended structures. We identify only three LAEs at
Widely considered a cornerstone of human morality, trust shapes many aspects of human social interactions. In this work, we present a theore… (voir plus)tical analysis of the
Background: We are witnessing an increasing adoption of machine learning (ML), especially deep learning (DL) algorithms in many software sys… (voir plus)tems, including safety-critical systems such as health care systems or autonomous driving vehicles. Ensuring the software quality of these systems is yet an open challenge for the research community, mainly due to the inductive nature of ML software systems. Traditionally, software systems were constructed deductively, by writing down the rules that govern the behavior of the system as program code. However, for ML software, these rules are inferred from training data. Few recent research advances in the quality assurance of ML systems have adapted different concepts from traditional software testing, such as mutation testing, to help improve the reliability of ML software systems. However, it is unclear if any of these proposed testing techniques from research are adopted in practice. There is little empirical evidence about the testing strategies of ML engineers. Aims: To fill this gap, we perform the first fine-grained empirical study on ML testing practices in the wild, to identify the ML properties being tested, the followed testing strategies, and their implementation throughout the ML workflow. Method: First, we systematically summarized the different testing strategies (e.g., Oracle Approximation), the tested ML properties (e.g., Correctness, Bias, and Fairness), and the testing methods (e.g., Unit test) from the literature. Then, we conducted a study to understand the practices of testing ML software. Results: In our findings: 1) we identified four (4) major categories of testing strategy including Grey-box, White-box, Black-box, and Heuristic-based techniques that are used by the ML engineers to find software bugs. 2) We identified 16 ML properties that are tested in the ML workflow.
There is increasing adoption of artificial intelligence in drug discovery. However, existing studies use machine learning to mainly utilize … (voir plus)the chemical structures of molecules but ignore the vast textual knowledge available in chemistry. Incorporating textual knowledge enables us to realize new drug design objectives, adapt to text-based instructions and predict complex biological activities. Here we present a multi-modal molecule structure-text model, MoleculeSTM, by jointly learning molecules' chemical structures and textual descriptions via a contrastive learning strategy. To train MoleculeSTM, we construct a large multi-modal dataset, namely, PubChemSTM, with over 280,000 chemical structure-text pairs. To demonstrate the effectiveness and utility of MoleculeSTM, we design two challenging zero-shot tasks based on text instructions, including structure-text retrieval and molecule editing. MoleculeSTM has two main properties: open vocabulary and compositionality via natural language. In experiments, MoleculeSTM obtains the state-of-the-art generalization ability to novel biochemical concepts across various benchmarks.