SynFlowNet: Towards Molecule Design with Guaranteed Synthesis Pathways
M. Cretu
Charles Harris
Julien Roy
Pietro Lio
TARIC-SLU: A Tunisian Benchmark Dataset for Spoken Language Understanding
Salima Mdhaffar
Fethi Bougares
Renato De Mori
Salah Zaiem
Yannick Estève
In recent years, there has been a significant increase in interest in developing Spoken Language Understanding (SLU) systems. SLU involves e… (see more)xtracting a list of semantic information from the speech signal. A major issue for SLU systems is the lack of sufficient amount of bi-modal (audio and textual semantic annotation) training data. Existing SLU resources are mainly available in high-resource languages such as English, Mandarin and French. However, one of the current challenges concerning low-resourced languages is data collection and annotation. In this work, we present a new freely available corpus, named TARIC-SLU, composed of railway transport conversations in Tunisian dialect that is continuously annotated in dialogue acts and slots. We describe the semantic model of the dataset, the data and experiments conducted to build ASR-based and SLU-based baseline models. To facilitate its use, a complete recipe, including data preparation, training and evaluation scripts, has been built and will be integrated to SpeechBrain, a popular open-source conversational AI toolkit based on PyTorch.
Temporal Graph Analysis with TGX
Razieh Shirzadkhani
Shenyang Huang
Elahe Kooshafar
Farimah Poursafaei
Real-world networks, with their evolving relations, are best captured as temporal graphs. However, existing software libraries are largely d… (see more)esigned for static graphs where the dynamic nature of temporal graphs is ignored. Bridging this gap, we introduce TGX, a Python package specially designed for analysis of temporal networks that encompasses an automated pipeline for data loading, data processing, and analysis of evolving graphs. TGX provides access to eleven built-in datasets and eight external Temporal Graph Benchmark (TGB) datasets as well as any novel datasets in the .csv format. Beyond data loading, TGX facilitates data processing functionalities such as discretization of temporal graphs and node subsampling to accelerate working with larger datasets. For comprehensive investigation, TGX offers network analysis by providing a diverse set of measures, including average node degree and the evolving number of nodes and edges per timestamp. Additionally, the package consolidates meaningful visualization plots indicating the evolution of temporal patterns, such as Temporal Edge Appearance (TEA) and Temporal Edge Trafficc (TET) plots. The TGX package is a robust tool for examining the features of temporal graphs and can be used in various areas like studying social networks, citation networks, and tracking user interactions. We plan to continuously support and update TGX based on community feedback. TGX is publicly available on: https://github.com/ComplexData-MILA/TGX.
On the consistency of hyper-parameter selection in value-based deep reinforcement learning
Johan Samir Obando Ceron
João Guilherme Madeira Araújo
Deep reinforcement learning (deep RL) has achieved tremendous success on various domains through a combination of algorithmic design and car… (see more)eful selection of hyper-parameters. Algorithmic improvements are often the result of iterative enhancements built upon prior approaches, while hyper-parameter choices are typically inherited from previous methods or fine-tuned specifically for the proposed technique. Despite their crucial impact on performance, hyper-parameter choices are frequently overshadowed by algorithmic advancements. This paper conducts an extensive empirical study focusing on the reliability of hyper-parameter selection for value-based deep reinforcement learning agents, including the introduction of a new score to quantify the consistency and reliability of various hyper-parameters. Our findings not only help establish which hyper-parameters are most critical to tune, but also help clarify which tunings remain consistent across different training regimes.
The Cost of Scaling Down Large Language Models: Reducing Model Size Affects Memory before In-context Learning.
Tian Jin
Nolan Clement
Xin Dong
Vaishnavh Nagarajan
Michael Carbin
Jonathan Ragan-Kelley
On the Societal Impact of Open Foundation Models
Sayash Kapoor
Rishi Bommasani
Kevin Klyman
Shayne Longpre
Ashwin Ramaswami
Peter Cihon
Aspen Hopkins
Kevin Bankston
Stella Biderman
Miranda Bogen
Rumman Chowdhury
Alex Engler
Peter Henderson
Yacine Jernite
Seth Lazar
Stefano Maffulli
Alondra Nelson
Aviya Skowron
Dawn Song … (see 5 more)
Victor Storchan
Daniel Zhang
Daniel E. Ho
Percy Liang
Arvind Narayanan
Towards a unified XAI-based framework for digital forensic investigations
Zainab Khalid
Farkhund Iqbal
Tree Broad Learning System for Small Data Modeling.
Heng Xia
Wen Yu
JunFei Qiao
Broad learning system based on neural network (BLS-NN) has poor efficiency for small data modeling with various dimensions. Tree-based BLS (… (see more)TBLS) is designed for small data modeling by introducing nondifferentiable modules and an ensemble strategy to the traditional broad learning system (BLS). TBLS replaces the neurons of BLS with the tree modules to map the input data. Moreover, we present three new TBLS variant methods and their incremental learning implementations, which are motivated by deep, broad, and ensemble learning. Their major distinction is reflected in the incremental learning strategies based on: 1) mean square error (mse); 2) pseudo-inverse; and 3) pseudo-inverse theory and stack representation. Therefore, this study further explores the domain of BLS based on the nondifferentiable modules. The simulations are compared with some state-of-the-art (SOTA) BLS-NN and tree methods under high-, medium-, and low-dimensional benchmark datasets. Results show that the proposed method outperforms the BLS-NN, and the modeling accuracy is remarkably improved with the small training data of the proposed TBLS.
Triage Software Update Impact via Release Notes Classification
Solomon Berhe
Vanessa Kan
Omhier Khan
Nathan Pader
Ali Zain Farooqui
Marc Maynard
Two Families of Indexable Partially Observable Restless Bandits and Whittle Index Computation
Nima Akbarzadeh
Uncertainty-aware hybrid paradigm of nonlinear MPC and model-based RL for offroad navigation: Exploration of transformers in the predictive model
Faraz Lotfi
Khalil Virji
Farnoosh Faraji
Lucas Berry
Andrew Holliday
In this paper, we investigate a hybrid scheme that combines nonlinear model predictive control (MPC) and model-based reinforcement learning … (see more)(RL) for navigation planning of an autonomous model car across offroad, unstructured terrains without relying on predefined maps. Our innovative approach takes inspiration from BADGR, an LSTM-based network that primarily concentrates on environment modeling, but distinguishes itself by substituting LSTM modules with transformers to greatly elevate the performance our model. Addressing uncertainty within the system, we train an ensemble of predictive models and estimate the mutual information between model weights and outputs, facilitating dynamic horizon planning through the introduction of variable speeds. Further enhancing our methodology, we incorporate a nonlinear MPC controller that accounts for the intricacies of the vehicle's model and states. The model-based RL facet produces steering angles and quantifies inherent uncertainty. At the same time, the nonlinear MPC suggests optimal throttle settings, striking a balance between goal attainment speed and managing model uncertainty influenced by velocity. In the conducted studies, our approach excels over the existing baseline by consistently achieving higher metric values in predicting future events and seamlessly integrating the vehicle's kinematic model for enhanced decision-making. The code and the evaluation data are available at https://github.com/FARAZLOTFI/offroad_autonomous_navigation/).
Understanding Intrinsic Socioeconomic Biases in Large Language Models
Mina Arzaghi
Florian Carichon
Large Language Models (LLMs) are increasingly integrated into critical decision-making processes, such as loan approvals and visa applicatio… (see more)ns, where inherent biases can lead to discriminatory outcomes. In this paper, we examine the nuanced relationship between demographic attributes and socioeconomic biases in LLMs, a crucial yet understudied area of fairness in LLMs. We introduce a novel dataset of one million English sentences to systematically quantify socioeconomic biases across various demographic groups. Our findings reveal pervasive socioeconomic biases in both established models such as GPT-2 and state-of-the-art models like Llama 2 and Falcon. We demonstrate that these biases are significantly amplified when considering intersectionality, with LLMs exhibiting a remarkable capacity to extract multiple demographic attributes from names and then correlate them with specific socioeconomic biases. This research highlights the urgent necessity for proactive and robust bias mitigation techniques to safeguard against discriminatory outcomes when deploying these powerful models in critical real-world applications.