Le traitement du langage naturel à l'ère de l'IA générative
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Publications
Structure-Aware Reinforcement Learning for Node-Overload Protection in Mobile Edge Computing
Mobile Edge Computing (MEC) involves placing computational capability and applications at the edge of the network, providing benefits such a… (voir plus)s reduced latency, reduced network congestion, and improved performance of applications. The performance and reliability of MEC degrades significantly when the edge server(s) in the cluster are overloaded. In this work, an adaptive admission control policy to prevent edge node from getting overloaded is presented. This approach is based on a recently-proposed low complexity RL (Reinforcement Learning) algorithm called SALMUT (Structure-Aware Learning for Multiple Thresholds), which exploits the structure of the optimal admission control policy in multi-class queues for an average-cost setting. We extend the framework to work for node overload-protection problem in a discounted-cost setting. The proposed solution is validated using several scenarios mimicking real-world deployments in two different settings — computer simulations and a docker testbed. Our empirical evaluations show that the total discounted cost incurred by SALMUT is similar to state-of-the-art deep RL algorithms such as PPO (Proximal Policy Optimization) and A2C (Advantage Actor Critic) but requires an order of magnitude less time to train, outputs easily interpretable policy, and can be deployed in an online manner.
2022-12-01
IEEE Transactions on Cognitive Communications and Networking (publié)
Abstract Computational approaches to the study of language emergence can help us understand how natural languages are shaped by cognitive an… (voir plus)d sociocultural factors. Previous work focused on tasks where agents refer to a single entity. In contrast, we study how agents predicate, that is, how they express that some relation holds between several entities. We introduce a setup where agents talk about a variable number of entities that can be partially observed by the listener. In the presence of a least-effort pressure, they tend to discuss only entities that are not observed by the listener. Thus we can obtain artificial phrases that denote a single entity, as well as artificial sentences that denote several entities. In natural languages, if we ignore the verb, phrases are usually concatenated, either in a specific order or by adding case markers to form sentences. Our setup allows us to quantify how much this holds in emergent languages using a metric we call concatenability. We also measure transitivity, which quantifies the importance of word order. We demonstrate the usefulness of this new setup and metrics for studying factors that influence argument structure. We compare agents having access to input representations structured into pre-segmented objects with properties, versus unstructured representations. Our results indicate that the awareness of object structure yields a more natural sentence organization.
2022-12-01
Transactions of the Association for Computational Linguistics (publié)
Software vulnerability detection is one of the most challenging tasks faced by reverse engineers. Recently, vulnerability detection has rece… (voir plus)ived a lot of attention due to a drastic increase in the volume and complexity of software. Reverse engineering is a time-consuming and labor-intensive process for detecting malware and software vulnerabilities. However, with the advent of deep learning and machine learning, it has become possible for researchers to automate the process of identifying potential security breaches in software by developing more intelligent technologies. In this research, we propose VDGraph2Vec, an automated deep learning method to generate representations of assembly code for the task of vulnerability detection. Previous approaches failed to attend to topological characteristics of assembly code while discovering the weakness in the software. VDGraph2Vec embeds the control flow and semantic information of assembly code effectively using the expressive capabilities of message passing neural networks and the RoBERTa model. Our model is able to learn the important features that help distinguish between vulnerable and non-vulnerable software. We carry out our experimental analysis for performance benchmark on three of the most common weaknesses and demonstrate that our model can identify vulnerabilities with high accuracy and outperforms the current state-of-the-art binary vulnerability detection models.
2022-12-01
International Conference on Machine Learning and Applications (publié)
The white matter is organized into “tracts” or “bundles,” which connect different parts of the central nervous system. Knowing where… (voir plus) these tracts are located in each individual is important for understanding the cause of potential sensorial, motor or cognitive deficits and for developing appropriate treatments. Traditionally, tracts are found using tracer injection, which is a difficult, slow and poorly scalable technique. However, axon populations from a given tract exhibit specific characteristics in terms of morphometrics and myelination. Hence, the delineation of tracts could, in principle, be done based on their morphometry. The objective of this study was to generate automatic parcellation of the rat spinal white matter tracts using the manifold information from scanning electron microscopy images of the entire spinal cord. The axon morphometrics (axon density, axon diameter, myelin thickness and g-ratio) were computed pixelwise following automatic axon segmentation using AxonSeg. The parcellation was based on an agglomerative clustering algorithm to group the tracts. Results show that axon morphometrics provide sufficient information to automatically identify some white matter tracts in the spinal cord, however, not all tracts were correctly identified. Future developments of microstructure quantitative MRI even bring hope for a personalized clustering of white matter tracts in each individual patient. The generated atlas and the associated code can be found at https://github.com/neuropoly/tract-clustering.
Rapid development of large-scale pre-training has resulted in foundation models that can act as effective feature extractors on a variety of… (voir plus) downstream tasks and domains. Motivated by this, we study the efficacy of pre-trained vision models as a foundation for downstream continual learning (CL) scenarios. Our goal is twofold. First, we want to understand the compute-accuracy trade-off between CL in the raw-data space and in the latent space of pre-trained encoders. Second, we investigate how the characteristics of the encoder, the pre-training algorithm and data, as well as of the resulting latent space affect CL performance. For this, we compare the efficacy of various pre-trained models in large-scale benchmarking scenarios with a vanilla replay setting applied in the latent and in the raw-data space. Notably, this study shows how transfer, forgetting, task similarity and learning are dependent on the input data characteristics and not necessarily on the CL algorithms. First, we show that under some circumstances reasonable CL performance can readily be achieved with a non-parametric classifier at negligible compute. We then show how models pre-trained on broader data result in better performance for various replay sizes. We explain this with representational similarity and transfer properties of these representations. Finally, we show the effectiveness of self-supervised pre-training for downstream domains that are out-of-distribution as compared to the pre-training domain. We point out and validate several research directions that can further increase the efficacy of latent CL including representation ensembling. The diverse set of datasets used in this study can serve as a compute-efficient playground for further CL research. We will publish the code.
2022-11-28
Proceedings of The 1st Conference on Lifelong Learning Agents (publié)