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Found 15 papers, 4 papers with code
Adapting Large Language Model with Speech for Fully Formatted End-to-End Speech Recognition
Most end-to-end (E2E) speech recognition models are composed of encoder and decoder blocks that perform acoustic and language modeling functions.
Improving Transformer-based Speech Recognition Using Unsupervised Pre-training
Speech recognition technologies are gaining enormous popularity in various industrial applications.
LMDrive: Closed-Loop End-to-End Driving with Large Language Models
On the other hand, previous autonomous driving methods tend to rely on limited-format inputs (e. g. sensor data and navigation waypoints), restricting the vehicle's ability to understand language information and interact with humans.
From General to Specific: Informative Scene Graph Generation via Balance Adjustment
The scene graph generation (SGG) task aims to detect visual relationship triplets, i. e., subject, predicate, object, in an image, providing a structural vision layout for scene understanding.
Adversarial Examples for Electrocardiograms
For this model, we utilized a new technique to generate smoothed examples to produce signals that are 1) indistinguishable to cardiologists from the original examples and 2) incorrectly classified by the neural network.
ABEM: An Adaptive Agent-based Evolutionary Approach for Mining Influencers in Online Social Networks
A key step in influence maximization in online social networks is the identification of a small number of users, known as influencers, who are able to spread influence quickly and widely to other users.
The feasibility of Q-band millimeter wave on hand-gesture recognition for indoor FTTR scenario
The generalization for different scenarios and dif-ferent users is an urgent problem for millimeter wave gesture recognition for indoor fiber-to-the-room (FTTR) scenario.
Building High-accuracy Multilingual ASR with Gated Language Experts and Curriculum Training
We propose gated language experts and curriculum training to enhance multilingual transformer transducer models without requiring language identification (LID) input from users during inference.
Code-Switching Text Generation and Injection in Mandarin-English ASR
Code-switching speech refers to a means of expression by mixing two or more languages within a single utterance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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A dynamic risk score for early prediction of cardiogenic shock using machine learning
We developed a deep learning-based risk stratification tool, called CShock, for patients admitted into the cardiac ICU with acute decompensated heart failure and/or myocardial infarction to predict onset of cardiogenic shock.
BHEISR: Nudging from Bias to Balance -- Promoting Belief Harmony by Eliminating Ideological Segregation in Knowledge-based Recommendations
This approach endeavors to transcend the constraints of the filter bubble, enrich recommendation diversity, and strike a belief balance among users while also catering to user preferences and system-specific business requirements.
Informative Scene Graph Generation via Debiasing
It integrates two components: Semantic Debiasing (SD) and Balanced Predicate Learning (BPL), for these imbalances.
Transformer Compression via Subspace Projection
By projecting the whole transform model into a subspace, we enable matrix operations between the weight matrices in the model and features in a reduced-dimensional space, leading to significant reductions in model parameters and computing resources.
Compressing Large Language Models by Streamlining the Unimportant Layer
Based on this phenomenon, we propose LLM-Streamline, which consists of two parts: layer pruning, where we remove a set of consecutive layers with the lowest importance in the model according to the target sparsity; and layer replacement, where we train a lightweight model to substitute the pruned layers, thereby mitigating the performance degradation caused by pruning.
Balancing Information Perception with Yin-Yang: Agent-Based Information Neutrality Model for Recommendation Systems
While preference-based recommendation algorithms effectively enhance user engagement by recommending personalized content, they often result in the creation of ``filter bubbles''.
