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Concept

Modèle de Markov caché

Publications associées (31)

Learning Analytics for Adaptive and Self-Improving Learning Environments for Inductive Teaching

Louis Pierre Faucon

The emergence of digital technology is changing education in many ways. A particularly interesting aspect of this transformation is the development of learning environments that can automatically adapt to individual students and can collect data in order t ...

EPFL2021

On quantifying the quality of acoustic models in hybrid DNN-HMM ASR

Hervé Bourlard, Afsaneh Asaei, Pranay Dighe

We propose an information theoretic framework for quantitative assessment of acoustic models used in hidden Markov model (HMM) based automatic speech recognition (ASR). The HMM backend expects that (i) the acoustic model yields accurate state conditional e ...

ELSEVIER2020

Predicting learners' effortful behaviour in adaptive assessment using multimodal data

Kshitij Sharma, Jennifer Kaitlyn Olsen

Many factors influence learners' performance on an activity beyond the knowledge required. Learners' on-task effort has been acknowledged for strongly relating to their educational outcomes, reflecting how actively they are engaged in that activity. Howeve ...

ASSOC COMPUTING MACHINERY2020

Multilingual Training and Adaptation in Speech Recognition

Sibo Tong

State-of-the-art acoustic models for Automatic Speech Recognition (ASR) are based on Hidden Markov Models (HMM) and Deep Neural Networks (DNN) and often require thousands of hours of transcribed speech data during training. Therefore, building multilingual ...

EPFL2020

An HMM Approach with Inherent Model Selection for Sign Language and Gesture Recognition

Mathew Magimai Doss, Sandrine Tornay

HMMs have been the one of the first models to be applied for sign recognition and have become the baseline models due to their success in modeling sequential and multivariate data. Despite the extensive use of HMMs for sign recognition, determining the HMM ...

EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA2020

Inference methods for the study of interacting biological oscillators in single-cells

Colas Noé Droin

Biological oscillators are pervasive in biology, covering all aspects of life from enzyme kinetics reactions to population dynamics. Although their behaviour has been intensively studied in the last decades, the recent advances of high-throughput experimen ...

EPFL2020

Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Networks

Emmanuel Abbé

We derive generalization and excess risk bounds for neural networks using a family of complexity measures based on a multilevel relative entropy. The bounds are obtained by introducing the notion of generated hierarchical coverings of neural networks and b ...

Microtome Publishing2020

Buffer Placement and Sizing for High-Performance Dataflow Circuits

Paolo Ienne, Andrea Guerrieri, Lana Josipovic

Commercial high-level synthesis tools typically produce statically scheduled circuits. Yet, effective C-to-circuit conversion of arbitrary software applications calls for dataflow circuits, as they can handle efficiently variable latencies (e.g., caches) a ...

ASSOC COMPUTING MACHINERY2020

Moire Flat Bands in Twisted Double Bilayer Graphene

Oleg Yazyev, Quansheng Wu, Seyedeh Fatemeh Haddadi Givi

We investigate twisted double bilayer graphene (TDBG), a four-layer system composed of two AB-stacked graphene bilayers rotated with respect to each other by a small angle. Our ab initio band structure calculations reveal a considerable energy gap at the c ...

AMER CHEMICAL SOC2020

Generating Master Faces for Use in PerformingWolf Attacks on Face Recognition Systems

Sébastien Marcel

Due to its convenience, biometric authentication, especial face authentication, has become increasingly mainstream and thus is now a prime target for attackers. Presentation attacks and face morphing are typical types of attack. Previous research has shown ...

IEEE2020

Constrained expectation-maximization algorithm for stochastic inertial error modeling: study of feasibility

Jan Skaloud, Stéphane Guerrier, Yannick Stebler

Stochastic modeling is a challenging task for low-cost sensors whose errors can have complex spectral structures. This makes the tuning process of the INS/GNSS Kalman filter often sensitive and difficult. For example, first-order Gauss–Markov processes are ...

IOP Publishing2011