Search Results for author:
Found 7 papers, 1 papers with code
Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models
Large Language Models (LLMs) have become a cornerstone in the field of Natural Language Processing (NLP), offering transformative capabilities in understanding and generating human-like text.
Representation Learning in Deep RL via Discrete Information Bottleneck
Several self-supervised representation learning methods have been proposed for reinforcement learning (RL) with rich observations.
A Vocabulary-Free Multilingual Neural Tokenizer for End-to-End Task Learning
Additionally, the dependency on a fixed vocabulary limits the subword models' adaptability across languages and domains.
MuMu: Cooperative Multitask Learning-based Guided Multimodal Fusion
However, it is challenging to extract robust multimodal representations due to the heterogeneous characteristics of data from multimodal sensors and disparate human activities, especially in the presence of noisy and misaligned sensor data.
Ranked #1 on
Multimodal Activity Recognition
on MMAct
Multi-GAT: A Graphical Attention-based Hierarchical Multimodal Representation Learning Approach for Human Activity Recognition
Finally, the experimental results with noisy sensor data indicate that Multi-GAT consistently outperforms all the evaluated baselines.
Ranked #2 on
Multimodal Activity Recognition
on MMAct
Human Activity Recognition
Multimodal Activity Recognition
+1
HAMLET: A Hierarchical Multimodal Attention-based Human Activity Recognition Algorithm
We develop a novel multimodal attention mechanism for disentangling and fusing the salient unimodal features to compute the multimodal features in the upper layer.
A Gray Box Interpretable Visual Debugging Approach for Deep Sequence Learning Model
In this work, we have developed a visual interactive web application, namely d-DeVIS, which helps to visualize the internal reasoning of the learning model which is trained on the audio data.
