Search Results for author:
Found 16 papers, 6 papers with code
Mixture of Scales: Memory-Efficient Token-Adaptive Binarization for Large Language Models
Binarization, which converts weight parameters to binary values, has emerged as an effective strategy to reduce the size of large language models (LLMs).
QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference
We introduce QUICK, a group of novel optimized CUDA kernels for the efficient inference of quantized Large Language Models (LLMs).
SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks
In this paper, we introduce SLEB, a novel approach designed to streamline LLMs by eliminating redundant transformer blocks.
Squeezing Large-Scale Diffusion Models for Mobile
The emergence of diffusion models has greatly broadened the scope of high-fidelity image synthesis, resulting in notable advancements in both practical implementation and academic research.
OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models
Large language models (LLMs) with hundreds of billions of parameters require powerful server-grade GPUs for inference, limiting their practical deployment.
Cross-speaker Emotion Transfer by Manipulating Speech Style Latents
In recent years, emotional text-to-speech has shown considerable progress.
Text-driven Emotional Style Control and Cross-speaker Style Transfer in Neural TTS
Expressive text-to-speech has shown improved performance in recent years.
GP22: A Car Styling Dataset for Automotive Designers
Therefore, we release GP22, composed of car styling features defined by automotive designers.
EdiTTS: Score-based Editing for Controllable Text-to-Speech
We present EdiTTS, an off-the-shelf speech editing methodology based on score-based generative modeling for text-to-speech synthesis.
Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network
In this paper, we propose a new sparse matrix format in order to enable a highly parallel decoding process of the entire sparse matrix.
Large-scale Speaker Retrieval on Random Speaker Variability Subspace
In this paper, we proposed Random Speaker-variability Subspace (RSS) projection to map a data into LSH based hash tables.
Learning pronunciation from a foreign language in speech synthesis networks
First, we train the speech synthesis network bilingually in English and Korean and analyze how the network learns the relations of phoneme pronunciation between the languages.
Robust and fine-grained prosody control of end-to-end speech synthesis
We propose prosody embeddings for emotional and expressive speech synthesis networks.
Voice Imitating Text-to-Speech Neural Networks
We propose a neural text-to-speech (TTS) model that can imitate a new speaker's voice using only a small amount of speech sample.
Viterbi-based Pruning for Sparse Matrix with Fixed and High Index Compression Ratio
Hence, pruning is usually restricted to inference with a batch size of one, for which an efficient parallel matrix-vector multiplication method exists.
Deep Neural Network Optimized to Resistive Memory with Nonlinear Current-Voltage Characteristics
Artificial Neural Network computation relies on intensive vector-matrix multiplications.
