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Found 9 papers, 3 papers with code
Interactive Reinforcement Learning for Table Balancing Robot
With the development of robotics, the use of robots in daily life is increasing, which has led to the need for anyone to easily train robots to improve robot use.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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A Design Space for Intelligent and Interactive Writing Assistants
In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities.
SoTTA: Robust Test-Time Adaptation on Noisy Data Streams
To address this problem, we present Screening-out Test-Time Adaptation (SoTTA), a novel TTA algorithm that is robust to noisy samples.
RPLKG: Robust Prompt Learning with Knowledge Graph
To improve the generalization performance on few-shot learning, there have been diverse efforts, such as prompt learning and adapter.
Towards Explainable AI Writing Assistants for Non-native English Speakers
We highlight the challenges faced by non-native speakers when using AI writing assistants to paraphrase text.
NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation
Test-time adaptation (TTA) is an emerging paradigm that addresses distributional shifts between training and testing phases without additional data acquisition or labeling cost; only unlabeled test data streams are used for continual model adaptation.
Learning Fair Representation via Distributional Contrastive Disentanglement
We provide a new type of contrastive loss motivated by Gaussian and Student-t kernels for distributional contrastive learning with theoretical analysis.
MuSE-SVS: Multi-Singer Emotional Singing Voice Synthesizer that Controls Emotional Intensity
We propose a multi-singer emotional singing voice synthesizer, Muse-SVS, that expresses emotion at various intensity levels by controlling subtle changes in pitch, energy, and phoneme duration while accurately following the score.
DAPPER: Label-Free Performance Estimation after Personalization for Heterogeneous Mobile Sensing
However, various factors such as different users, devices, and environments impact the performance of such applications, thus making the domain shift (i. e., distributional shift between the training domain and the target domain) a critical issue in mobile sensing.
