Search Results for author: Pattie Maes

Found 10 papers, 8 papers with code

Txt2Vid: Ultra-Low Bitrate Compression of Talking-Head Videos via Text

1 code implementation26 Jun 2021 Pulkit Tandon, Shubham Chandak, Pat Pataranutaporn, Yimeng Liu, Anesu M. Mapuranga, Pattie Maes, Tsachy Weissman, Misha Sra

Video represents the majority of internet traffic today, driving a continual race between the generation of higher quality content, transmission of larger file sizes, and the development of network infrastructure.

Talking Face Generation Talking Head Generation +2

Pretrained Encoders are All You Need

1 code implementation ICML Workshop URL 2021 Mina Khan, P Srivatsa, Advait Rane, Shriram Chenniappa, Rishabh Anand, Sherjil Ozair, Pattie Maes

Data-efficiency and generalization are key challenges in deep learning and deep reinforcement learning as many models are trained on large-scale, domain-specific, and expensive-to-label datasets.

Contrastive Learning reinforcement-learning +1

Personalizing Pre-trained Models

no code implementations2 Jun 2021 Mina Khan, P Srivatsa, Advait Rane, Shriram Chenniappa, Asadali Hazariwala, Pattie Maes

Self-supervised or weakly supervised models trained on large-scale datasets have shown sample-efficient transfer to diverse datasets in few-shot settings.

Continual Learning Few-Shot Learning +2

PAL: Intelligence Augmentation using Egocentric Visual Context Detection

no code implementations22 May 2021 Mina Khan, Pattie Maes

We created a wearable system, called PAL, for wearable, personalized, and privacy-preserving egocentric visual context detection.

Face Detection Privacy Preserving

Uncertainty-Aware Boosted Ensembling in Multi-Modal Settings

1 code implementation21 Apr 2021 Utkarsh Sarawgi, Rishab Khincha, Wazeer Zulfikar, Satrajit Ghosh, Pattie Maes

Reliability of machine learning (ML) systems is crucial in safety-critical applications such as healthcare, and uncertainty estimation is a widely researched method to highlight the confidence of ML systems in deployment.

Prediction Intervals

Robustness to Missing Features using Hierarchical Clustering with Split Neural Networks

1 code implementation19 Nov 2020 Rishab Khincha, Utkarsh Sarawgi, Wazeer Zulfikar, Pattie Maes

In this work, we propose a simple yet effective approach that clusters similar input features together using hierarchical clustering and then trains proportionately split neural networks with a joint loss.

Imputation

Uncertainty-Aware Multi-Modal Ensembling for Severity Prediction of Alzheimer's Dementia

1 code implementation3 Oct 2020 Utkarsh Sarawgi, Wazeer Zulfikar, Rishab Khincha, Pattie Maes

Reliability in Neural Networks (NNs) is crucial in safety-critical applications like healthcare, and uncertainty estimation is a widely researched method to highlight the confidence of NNs in deployment.

severity prediction

Why have a Unified Predictive Uncertainty? Disentangling it using Deep Split Ensembles

1 code implementation25 Sep 2020 Utkarsh Sarawgi, Wazeer Zulfikar, Rishab Khincha, Pattie Maes

Our work further demonstrates its applicability in a multi-modal setting using a benchmark Alzheimer's dataset and also shows how deep split ensembles can highlight hidden modality-specific biases.

Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severity

1 code implementation30 Aug 2020 Utkarsh Sarawgi, Wazeer Zulfikar, Nouran Soliman, Pattie Maes

Our system achieves state-of-the-art test accuracy, precision, recall, and F1-score of 83. 3% each for AD classification, and state-of-the-art test root mean squared error (RMSE) of 4. 60 for MMSE score regression.

Transfer Learning

Real-Time Sleep Staging using Deep Learning on a Smartphone for a Wearable EEG

2 code implementations25 Nov 2018 Abhay Koushik, Judith Amores, Pattie Maes

We present the first real-time sleep staging system that uses deep learning without the need for servers in a smartphone application for a wearable EEG.

EEG General Classification +1

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