Revisiting Offline Compression: Going Beyond Factorization-based Methods for Transformer Language Models

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The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Accurate human trajectory prediction is crucial for applications such as autonomous vehicles, robotics, and surveillance systems. Yet, existing models often fail to fully leverage the non-verbal social cues human subconsciously communicate when navigating ...

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict t ...

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimen ...

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Current transformer-based skeletal action recognition models tend to focus on a limited set of joints and low-level motion patterns to predict action classes. This results in significant performance degradation under small skeleton perturbations or changin ...

Intelligent Tutoring Systems (ITS) enhance personalized learning by predicting student answers to provide immediate and customized instruction. However, recent research has primarily focused on the correctness of the answer rather than the student's perfor ...

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Adversarial attacks in Natural Language Processing apply perturbations in the character or token levels. Token-level attacks, gaining prominence for their use of gradient-based methods, are susceptible to altering sentence semantics, leading to invalid adv ...

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Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these robus ...

The increasing complexity of transformer models in artificial intelligence expands their computational costs, memory usage, and energy consumption. Hardware acceleration tackles the ensuing challenges by designing processors and accelerators tailored for t ...

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Social media is increasingly being employed to develop Cultural Ecosystem Services (CES) indicators. The image-sharing platform Flickr has been one of the most popular sources of data. Most large-scale studies, however, tend to only use the number of image ...

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

Revisiting Offline Compression: Going Beyond Factorization-based Methods for Transformer Language Models

Chattez avec Graph Search

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Social-Transmotion: Promptable Human Trajectory Prediction

Driving and suppressing the human language network using large language models

The multimodality cell segmentation challenge: toward universal solutions

Random matrix methods for high-dimensional machine learning models

Fly-Swat or Cannon? Cost-Effective Language Model Choice via Meta-Modeling

Text-to-Microstructure Generation Using Generative Deep Learning

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning

Student Answer Forecasting: Transformer-Driven Answer Choice Prediction for Language Learning

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

GELEX: Generative AI-Hybrid System for Example-Based Learning

Network time series forecasting in photovoltaics power production

Sparse autoregressive neural networks for classical spin systems

Revisiting Character-level Adversarial Attacks for Language Models

Aggregating Spatial and Photometric Context for Photometric Stereo

Robust NAS under adversarial training: benchmark, theory, and beyond

Accelerator-driven Data Arrangement to Minimize Transformers Run-time on Multi-core Architectures

Topics in statistical physics of high-dimensional machine learning

Understanding the sentiment associated with cultural ecosystem services using images and text from social media

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms