Search Results for author:
Found 17 papers, 3 papers with code
Detect Profane Language in Streaming Services to Protect Young Audiences
With the rapid growth of online video streaming, recent years have seen increasing concerns about profane language in their content.
Crowdsourcing-Based Knowledge Graph Construction for Drug Side Effects Using Large Language Models with an Application on Semaglutide
Social media is a rich source of real-world data that captures valuable patient experience information for pharmacovigilance.
Toward Equitable Access: Leveraging Crowdsourced Reviews to Investigate Public Perceptions of Health Resource Accessibility
Our findings reveal that public perceptions of health resource accessibility varied significantly across the U. S., with disparities peaking during the pandemic and slightly easing post-crisis.
Towards Single-Lens Controllable Depth-of-Field Imaging via Depth-Aware Point Spread Functions
A Depth-aware Controllable DoF Imaging (DCDI) framework is proposed equipped with All-in-Focus (AiF) aberration correction and monocular depth estimation, where the recovered image and corresponding depth map are utilized to produce imaging results under diverse DoFs of any high-end lens via patch-wise convolution.
End-to-end spoken language understanding using joint CTC loss and self-supervised, pretrained acoustic encoders
It is challenging to extract semantic meanings directly from audio signals in spoken language understanding (SLU), due to the lack of textual information.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Dialog act guided contextual adapter for personalized speech recognition
Specifically, it leverages dialog acts to select the most relevant user catalogs and creates queries based on both -- the audio as well as the semantic relationship between the carrier phrase and user catalogs to better guide the contextual biasing.
2nd Place Solution to Google Landmark Retrieval 2020
Using this method, we got a public score of 0. 40176 and a private score of 0. 36278 and achieved 2nd place in the Google Landmark Retrieval Competition 2020.
A neural prosody encoder for end-ro-end dialogue act classification
Dialogue act classification (DAC) is a critical task for spoken language understanding in dialogue systems.
Multi-task RNN-T with Semantic Decoder for Streamable Spoken Language Understanding
In addition, the NLU model in the two-stage system is not streamable, as it must wait for the audio segments to complete processing, which ultimately impacts the latency of the SLU system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Attentive Contextual Carryover for Multi-Turn End-to-End Spoken Language Understanding
Recent years have seen significant advances in end-to-end (E2E) spoken language understanding (SLU) systems, which directly predict intents and slots from spoken audio.
Natural Language Understanding
Spoken Language Understanding
Encoding Syntactic Knowledge in Transformer Encoder for Intent Detection and Slot Filling
We propose a novel Transformer encoder-based architecture with syntactical knowledge encoded for intent detection and slot filling.
Semi-Supervised Recognition under a Noisy and Fine-grained Dataset
One of the difficulties of this competition is how to use unlabeled data.
Ranked #1 on
Image Classification
on ImageNet
(Hardware Burden metric)
Jensen: An Easily-Extensible C++ Toolkit for Production-Level Machine Learning and Convex Optimization
This paper introduces Jensen, an easily extensible and scalable toolkit for production-level machine learning and convex optimization.
Modeling and Simultaneously Removing Bias via Adversarial Neural Networks
In real world systems, the predictions of deployed Machine Learned models affect the training data available to build subsequent models.
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications
In the present paper, we bridge this gap, by proposing several new algorithms (including greedy, majorization-minimization, minorization-maximization, and relaxation algorithms) that not only scale to large datasets but that also achieve theoretical approximation guarantees comparable to the state-of-the-art.
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications to Parallel Machine Learning and Multi-Label Image Segmentation
While the robust versions have been studied in the theory community, existing work has focused on tight approximation guarantees, and the resultant algorithms are not, in general, scalable to very large real-world applications.
