Search Results for author:
Found 17 papers, 3 papers with code
Collaborative Data Relabeling for Robust and Diverse Voice Apps Recommendation in Intelligent Personal Assistants
In this paper, we introduce a fallback skill recommendation system to suggest a voice app to a customer for an unhandled voice command.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Net 835-Gb/s/λ Carrier- and LO-Free 100-km Transmission Using Channel-Aware Phase Retrieval Reception
We experimentally demonstrate the first carrier- and LO-free 800G/{\lambda} receiver enabling direct compatibility with standard coherent transmitters via phase retrieval, achieving net 835-Gb/s transmission over 100-km SMF and record 8. 27-b/s/Hz net optical spectral efficiency.
Can Small Language Models Help Large Language Models Reason Better?: LM-Guided Chain-of-Thought
The Frozen large LM is then prompted to predict a task output based on the rationale generated by the lightweight LM.
Toward Informal Language Processing: Knowledge of Slang in Large Language Models
Our work offers a comprehensive evaluation and a high-quality benchmark on English slang based on the OpenSubtitles corpus, serving both as a publicly accessible resource and a platform for applying tools for informal language processing.
Faithful Model Evaluation for Model-Based Metrics
Statistical significance testing is used in natural language processing (NLP) to determine whether the results of a study or experiment are likely to be due to chance or if they reflect a genuine relationship.
Evaluating Large Language Models on Controlled Generation Tasks
While recent studies have looked into the abilities of large language models in various benchmark tasks, including question generation, reading comprehension, multilingual and etc, there have been few studies looking into the controllability of large language models on generation tasks.
FLIRT: Feedback Loop In-context Red Teaming
Here we propose an automatic red teaming framework that evaluates a given model and exposes its vulnerabilities against unsafe and inappropriate content generation.
Is the Elephant Flying? Resolving Ambiguities in Text-to-Image Generative Models
Natural language often contains ambiguities that can lead to misinterpretation and miscommunication.
Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method
In addition, this work fully considers the heterogeneity of SNs (i. e. differentiated sensing range and deployment cost) and three-dimensional (3-D) deployment scenarios.
Predicting vacant parking space availability zone-wisely: a graph based spatio-temporal prediction approach
Vacant parking space (VPS) prediction is one of the key issues of intelligent parking guidance systems.
OPP-Miner: Order-preserving sequential pattern mining
To discover patterns, existing methods often convert time series data into another form, such as nominal/symbolic format, to reduce dimensionality, which inevitably deviates the data values.
Two-stage Voice Application Recommender System for Unhandled Utterances in Intelligent Personal Assistant
Intelligent personal assistants (IPA) enable voice applications that facilitate people's daily tasks.
Attribute reduction and rule acquisition of formal decision context based on two new kinds of decision rules
The rule acquisition algorithms for I-decision rules and II-decision rules are presented.
A Detector-oblivious Multi-arm Network for Keypoint Matching
This paper presents a matching network to establish point correspondence between images.
Metric-Free Individual Fairness with Cooperative Contextual Bandits
Group fairness requires that different groups should be treated similarly which might be unfair to some individuals within a group.
Academic Performance Estimation with Attention-based Graph Convolutional Networks
Traditional methods for student's performance prediction usually neglect the underlying relationships between multiple courses; and how students acquire knowledge across them.
Reliable Deep Grade Prediction with Uncertainty Estimation
Prior research on students' grade prediction include shallow linear models; however, students' learning is a highly complex process that involves the accumulation of knowledge across a sequence of courses that can not be sufficiently modeled by these linear models.
