On the conversational persuasiveness of GPT-4

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Developing agents that can reliably act on our behalf is central to artificial intelligence (AI). These agents must seamlessly interact with tools, like search engines and databases, and collaborate. In this thesis, we study the abstractions, methods, and ...

Given a ground-level query image and a geo-referenced aerial image that covers the query’s local surroundings, fine-grained cross-view localization aims to estimate the location of the ground camera inside the aerial image. Recent works have focused on dev ...

Self-supervised pretrained models exhibit competitive performance in automatic speech recognition (ASR) on finetuning, even with limited in-domain supervised data. However, popular pretrained models are not suitable for streaming ASR because they are train ...

Faithful human performance capture and free-view rendering from sparse RGB observations is a long-standing problem in Vision and Graphics. The main challenges are the lack of observations and the inherent ambiguities of the setting, e.g. occlusions and dep ...

Recent works have identified a gap between research and practice in artificial intelligence security: threats studied in academia do not always reflect the practical use and security risks of AI. For example, while models are often studied in isolation, th ...

State-of-the-art results in large language models (LLMs) often rely on scale, which becomes computationally expensive. This has sparked a research agenda to reduce these models' parameter counts and computational costs without significantly impacting their ...

State-of-the-art face recognition models are computationally expensive for mobile applications. Training lightweight face recognition models also requires large identity-labeled datasets, raising privacy and ethical concerns. Generating synthetic datasets ...

In the rapidly evolving AI era with large language models (LLMs) at the core, making LLMs more trustworthy and efficient, especially in output generation (inference), has gained significant attention. This is to reduce plausible but faulty LLM outputs (a.k ...

This symposium Article discusses issues raised for patent processes and policy created by inventions generated by artificial intelligence (“AI”). The Article begins by examining the normative desirability of allowing patents on AI-generated inventions. Whi ...

Writing high-quality procedural texts is a challenging task for many learners. While example-based learning has shown promise as a feedback approach, a limitation arises when all learners receive the same content without considering their individual input ...

The field of text-to-video retrieval has advanced significantly with the evolution of language models and large-scale pre-training on generated caption-video pairs. Current methods predominantly focus on visual and event-based details, making retrieval lar ...

Under resource constraints, LLMs are usually fine-tuned with additional knowledge using Parameter Efficient Fine-Tuning (PEFT), using Low-Rank Adaptation (LoRA) modules. In fact, LoRA injects a new set of small trainable matrices to adapt an LLM to a new t ...

Large Language Models (LLMs) present a promising frontier in robotic task planning by leveraging extensive human knowledge. Nevertheless, the current literature often overlooks the critical aspects of robots' adaptability and error correction. This work ai ...

In this paper, we present EdgeFace - a lightweight and efficient face recognition network inspired by the hybrid architecture of EdgeNeXt. By effectively combining the strengths of both CNN and Transformer models, and a low rank linear layer, EdgeFace achi ...

The recent advance of large language models (LLMs) demonstrates that these large-scale foundation models achieve remarkable capabilities across a wide range of language tasks and domains. The success of the statistical learning approach challenges our unde ...

Laser Powder Bed Fusion (LPBF) is an Additive Manufacturing (AM) process consolidating parts layer by layer, from a metallic powder bed. It allows no limitation in terms of geometry and is therefore of particular interest to various industries. Metallic LP ...

By supporting the access of multiple memory words at the same time, Bit-line Computing (BC) architectures allow the parallel execution of bit-wise operations in-memory. At the array periphery, arithmetic operations are then derived with little additional o ...

The presence of conversational agents (or chatbots) in educational contexts has been steadily increasing over the past few years. Recent surveys have shown widespread interest in the use of chatbots in education, both for research and practice. Although th ...

The application of unsupervised domain adaptation (UDA)-based fault diagnosis methods has shown significant efficacy in industrial settings, facilitating the transfer of operational experience and fault signatures between different operating conditions, di ...

On the conversational persuasiveness of GPT-4

Chattez avec Graph Search

Collaborative AI Agents in the Era of Large Language Models

Adapting Fine-Grained Cross-View Localization to Areas Without Fine Ground Truth

XLSR-Transducer: Streaming ASR for Self-Supervised Pretrained Models

MetaCap: Meta-learning Priors from Multi-view Imagery for Sparse-View Human Performance Capture and Rendering

Towards more Practical Threat Models in Artificial Intelligence Security

Building on Efficient Foundations: Effectively Training LLMs with Structured Feedforward Layers

Knowledge Distillation for Face Recognition Using Synthetic Data With Dynamic Latent Sampling

Trustworthy and Efficient LLMs Meet Databases

AI-Generated Inventions: Implications for the Patent System

Enhancing Procedural Writing Through Personalized Example Retrieval: A Case Study on Cooking Recipes

Beyond Essentials: Nuanced and Diverse Text-to-video Retrieval

Beyond fine-tuning: LoRA modules boost near-OOD detection and LLM security

Action Contextualization: Adaptive Task Planning and Action Tuning Using Large Language Models

EdgeFace: Efficient Face Recognition Model for Edge Devices

Monotonicity Reasoning in the Age of Neural Foundation Models

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

Bit-Line Computing for CNN Accelerators Co-Design in Edge AI Inference

A Conceptual Framework for Integrating Conversational Agents in Digital Education

Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios