Search Results for author:
Found 17 papers, 0 papers with code
MergeME: Model Merging Techniques for Homogeneous and Heterogeneous MoEs
The recent success of specialized Large Language Models (LLMs) in domains such as mathematical reasoning and coding has led to growing interest in methods for merging these expert LLMs into a unified Mixture-of-Experts (MoE) model, with the goal of enhancing performance in each domain while retaining effectiveness on general tasks.
MAML-en-LLM: Model Agnostic Meta-Training of LLMs for Improved In-Context Learning
In this paper, we propose MAML-en-LLM, a novel method for meta-training LLMs, which can learn truly generalizable parameters that not only perform well on disjointed tasks but also adapts to unseen tasks.
A Robust Semantic Communication System for Image
Semantic communications have gained significant attention as a promising approach to address the transmission bottleneck, especially with the continuous development of 6G techniques.
Truthful Transaction Protocol for E-Commerce Networks Based on Double Auction
This screening algorithm is customer-oriented and offers personalized commodities by preventing unqualified sellers from participating in the transaction.
Towards Semantic Communications: Deep Learning-Based Image Semantic Coding
Semantic communications has received growing interest since it can remarkably reduce the amount of data to be transmitted without missing critical information.
Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems
We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9. 3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the Natural Language Understanding (NLU) component of a virtual assistant system.
Cross-Lingual Natural Language Inference
intent-classification
+5
A Robust Deep Learning Enabled Semantic Communication System for Text
With the advent of the 6G era, the concept of semantic communication has attracted increasing attention.
Semantic Communications: Principles and Challenges
Semantic communication, regarded as the breakthrough beyond the Shannon paradigm, aims at the successful transmission of semantic information conveyed by the source rather than the accurate reception of each single symbol or bit regardless of its meaning.
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications
The thriving of artificial intelligence (AI) applications is driving the further evolution of wireless networks.
Industry Scale Semi-Supervised Learning for Natural Language Understanding
This paper presents a production Semi-Supervised Learning (SSL) pipeline based on the student-teacher framework, which leverages millions of unlabeled examples to improve Natural Language Understanding (NLU) tasks.
Category-Adaptive Domain Adaptation for Semantic Segmentation
Unsupervised domain adaptation (UDA) becomes more and more popular in tackling real-world problems without ground truth of the target domain.
Perceptual Image Restoration with High-Quality Priori and Degradation Learning
We show that our model performs well in measuring the similarity between restored and degraded images.
Policy-Aware Mobility Model Explains the Growth of COVID-19 in Cities
With the continued spread of coronavirus, the task of forecasting distinctive COVID-19 growth curves in different cities, which remain inadequately explained by standard epidemiological models, is critical for medical supply and treatment.
Enhance Robustness of Sequence Labelling with Masked Adversarial Training
Adversarial training (AT) has shown strong regularization effects on deep learning algorithms by introducing small input perturbations to improve model robustness.
Ranked #3 on
Chunking
on CoNLL 2000
(using extra training data)
Delay Characterization of Mobile Edge Computing for 6G Time-Sensitive Services
Only a few research efforts have been devoted to other random delay characteristics, such as the delay bound violation probability and the probability distribution of the delay, by decoupling the transmission and computation processes of MEC.
SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling
Virtual adversarial training (VAT) is a powerful technique to improve model robustness in both supervised and semi-supervised settings.
Ranked #7 on
Chunking
on CoNLL 2000
Approximate Message Passing with Nearest Neighbor Sparsity Pattern Learning
We consider the problem of recovering clustered sparse signals with no prior knowledge of the sparsity pattern.
