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Found 8 papers, 2 papers with code
Detecting Bias and Enhancing Diagnostic Accuracy in Large Language Models for Healthcare
Biased AI-generated medical advice and misdiagnoses can jeopardize patient safety, making the integrity of AI in healthcare more critical than ever.
Resprompt: Residual Connection Prompting Advances Multi-Step Reasoning in Large Language Models
Breakdown analysis further highlights RESPROMPT particularly excels in complex multi-step reasoning: for questions demanding at least five reasoning steps, RESPROMPT outperforms the best CoT based benchmarks by a remarkable average improvement of 21. 1% on LLaMA-65B and 14. 3% on LLaMA2-70B.
On the Equivalence of Graph Convolution and Mixup
We establish this equivalence mathematically by demonstrating that graph convolution networks (GCN) and simplified graph convolution (SGC) can be expressed as a form of Mixup.
Prosody Transfer in Neural Text to Speech Using Global Pitch and Loudness Features
This paper presents a simple yet effective method to achieve prosody transfer from a reference speech signal to synthesized speech.
Learning Mixtures of Separable Dictionaries for Tensor Data: Analysis and Algorithms
This work addresses the problem of learning sparse representations of tensor data using structured dictionary learning.
Identifiability of Kronecker-structured Dictionaries for Tensor Data
This paper derives sufficient conditions for local recovery of coordinate dictionaries comprising a Kronecker-structured dictionary that is used for representing $K$th-order tensor data.
STARK: Structured Dictionary Learning Through Rank-one Tensor Recovery
In recent years, a class of dictionaries have been proposed for multidimensional (tensor) data representation that exploit the structure of tensor data by imposing a Kronecker structure on the dictionary underlying the data.
Minimax Lower Bounds for Kronecker-Structured Dictionary Learning
This paper finds fundamental limits on the sample complexity of estimating dictionaries for tensor data by proving a lower bound on the minimax risk.
