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Found 11 papers, 5 papers with code
CLaM-TTS: Improving Neural Codec Language Model for Zero-Shot Text-to-Speech
With the emergence of neural audio codecs, which encode multiple streams of discrete tokens from audio, large language models have recently gained attention as a promising approach for zero-shot Text-to-Speech (TTS) synthesis.
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
State-space models (SSMs), such as Mamba (Gu & Dao, 2023), have been proposed as alternatives to Transformer networks in language modeling, by incorporating gating, convolutions, and input-dependent token selection to mitigate the quadratic cost of multi-head attention.
A Simple Framework to Accelerate Multilingual Language Model for Monolingual Text Generation
Recent advancements in large language models have facilitated the execution of complex language tasks, not only in English but also in non-English languages.
SAiD: Speech-driven Blendshape Facial Animation with Diffusion
Speech-driven 3D facial animation is challenging due to the scarcity of large-scale visual-audio datasets despite extensive research.
Image Clustering Conditioned on Text Criteria
Classical clustering methods do not provide users with direct control of the clustering results, and the clustering results may not be consistent with the relevant criterion that a user has in mind.
Addressing Feature Imbalance in Sound Source Separation
Neural networks often suffer from a feature preference problem, where they tend to overly rely on specific features to solve a task while disregarding other features, even if those neglected features are essential for the task.
Predictive Pipelined Decoding: A Compute-Latency Trade-off for Exact LLM Decoding
This paper presents "Predictive Pipelined Decoding (PPD)," an approach that speeds up greedy decoding in Large Language Models (LLMs) while maintaining the exact same output as the original decoding.
Mini-Batch Optimization of Contrastive Loss
Contrastive learning has gained significant attention as a method for self-supervised learning.
Equal Experience in Recommender Systems
We explore the fairness issue that arises in recommender systems.
A Fair Classifier Using Kernel Density Estimation
As machine learning becomes prevalent in a widening array of sensitive applications such as job hiring and criminal justice, one critical aspect that machine learning classifiers should respect is to ensure fairness: guaranteeing the irrelevancy of a prediction output to sensitive attributes such as gender and race.
Wasserstein GAN Can Perform PCA
Generative Adversarial Networks (GANs) have become a powerful framework to learn generative models that arise across a wide variety of domains.
