3 citations · 5 across the 11 of their papers we have counts for
6 papers · 1 filter
MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM
Dongwei Wang, Jinhee Kim, Seokho Han +8
Dynamic runtime latency and memory constraints necessitate flexible large language model (LLM) deployment, where an LLM can be inferred with various quantization precisions based o…
ODE(ODE): Shortcutting the Time and the Length in Diffusion and Flow Models for Faster Sampling
Denis Gudovskiy, Wenzhao Zheng, Tomoyuki Okuno +2
Continuous normalizing flows (CNFs) and diffusion models (DMs) generate high-quality data from a noise distribution. However, their sampling process demands multiple iterations to…
SeRpEnt: Selective Resampling for Expressive State Space Models
Stefano Rando, Luca Romani, Matteo Migliarini +3
State Space Models (SSMs) have recently enjoyed a rise to prominence in the field of deep learning for sequence modeling, especially as an alternative to Transformers. Their succes…
DFM: Interpolant-free Dual Flow Matching
Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata
Continuous normalizing flows (CNFs) can model data distributions with expressive infinite-length architectures. But this modeling involves computationally expensive process of solv…
ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-Variable Context Encoding
Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata
Normalizing flow-based generative models have been widely used in applications where the exact density estimation is of major importance. Recent research proposes numerous methods…
Split-Ensemble: Efficient OOD-aware Ensemble via Task and Model Splitting
Anthony Chen, Huanrui Yang, Yulu Gan +7
Uncertainty estimation is crucial for machine learning models to detect out-of-distribution (OOD) inputs. However, the conventional discriminative deep learning classifiers produce…