Visual speech recognition : from traditional to deep learning frameworks

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Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

This study examines the acoustic features of Parkinson's patients with depression. More specifically, the research investigates whether interpretable, handcrafted acoustic feature-based methods, previously used for automatic speech-based depression detecti ...

Recently, cutting-edge brain-machine interfaces (BMIs) have revealed the potential of decoders such as recurrent neural networks (RNNs) in predicting attempted handwriting [1] or speech [2], enabling rapid communication recovery after paralysis. However, c ...

Animals have to navigate complex environments and perform intricate swimming maneuvers in the real world. To conquer these challenges, animals evolved a variety of motion control strategies. While it is known that many factors contribute to motion control, ...

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

Real-time bioaerosol monitoring is improving the quality of life for people affected by allergies, but it often relies on deep learning models which pose challenges for widespread adoption. These models are typically trained in a supervised fashion and req ...

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

The Internet of Things (IoT) consists of physical objects and devices embedded with network connectivity, software, and sensors to collect and transmit data. The development of the Internet of Things (IoT) has led to various security and privacy issues, in ...

Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal calls to oracles (computational property predictors). This problem becomes more apparen ...

“You never forget how to ride a bike”, – but how is that possible? The brain is able to learn complex skills, stop the practice for years, learn other skills in between, and still retrieve the original knowledge when necessary. The mechanisms of this capab ...

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

FDTD/FEM simulations, strengthened by machine learning, achieve 95.44% optical absorption efficiency in TiO 2 /TiN plasmonic nanocavities for solar energy harvesting. ...

The invention relates to an Imaging sensor device in a stacked arrangement comprising: - a pixel array tier comprising a plurality of pixel segments each having a plurality of pixels for photon detection each providing a digital pixel output; - a processin ...

Recent advances in machine learning have dramatically increased model size and computational requirements, increasingly straining computing system capabilities. This tension is particularly acute for resource-constrained edge scenarios, for which careful h ...

Smarty is a fully-reconfigurable on-chip feed-forward artificial neural network (ANN) with ten integrated time-to-digital converters (TDCs) designed in a 16 nm FinFET CMOS technology node. The integration of TDCs together with an ANN aims to reduce system ...

Rigid body interactions are fundamental to numerous scientific disciplines, but remain challenging to simulate due to their abrupt nonlinear nature and sensitivity to complex, often unknown environmental factors. These challenges call for adaptable learnin ...

Driven by the demand for real-time processing and the need to minimize latency in AI algorithms, edge computing has experienced remarkable progress. Decision-making AI applications stand out for their heavy reliance on data-centric operations, predominantl ...

Magnonics is a research field that has gained an increasing interest in both the fundamental and applied sciences in recent years. This field aims to explore and functionalize collective spin excitations in magnetically ordered materials for modern informa ...

Traditional example-based learning methods are often limited by static, expert-created content. Hence, they face challenges in scalability, engagement, and effectiveness, as some learners might struggle to relate to the examples or find them relevant. To a ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

Visual speech recognition : from traditional to deep learning frameworks

Chattez avec Graph Search

Predicting the long-term collective behaviour of fish pairs with deep learning

Exploring the Complexity of Parkinson's Patient Speech for Depression Detection task: A Qualitative Analysis

33.3 MiBMI: A 192/512-Channel 2.46mm² Miniaturized Brain-Machine Interface Chipset Enabling 31-Class Brain-to-Text Conversion Through Distinctive Neural Codes

Using deep reinforcement learning to investigate stretch feedback during swimming of the lamprey

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Self-supervised and few-shot learning for robust bioaerosol monitoring

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

GraphFedAI framework for DDoS attack detection in IoT systems using federated learning and graph based artificial intelligence

Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning

Context selectivity with dynamic availability enables lifelong continual learning

Understanding generalization and robustness in modern deep learning

Machine-learning-empowered FDTD/FEM simulations for predictive solar energy absorption in plasmonic metamaterial nanocavity arrays

Imaging sensor device using an array of single-photon avalanche diode photodetectors

Keep All in Memory with Maxwell: a Near-SRAM Computing Architecture for Edge AI Applications

On-Chip Fully Reconfigurable Artificial Neural Network in 16 nm FinFET for Positron Emission Tomography

Integrating Physics and Topology in Neural Networks for Learning Rigid Body Dynamics

EdgeAI-Aware Design of In-Memory Computing Architectures

The 2024 Magnonics Roadmap

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

Aggregating Spatial and Photometric Context for Photometric Stereo