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Found 11 papers, 5 papers with code
Incentivizing Reasoning for Advanced Instruction-Following of Large Language Models
First, we stem from the decomposition of complex instructions under existing taxonomies and propose a reproducible data acquisition method.
Scaling Laws for Speculative Decoding
The escalating demand for efficient decoding in large language models (LLMs) is particularly critical for reasoning-intensive architectures like OpenAI-o3 and DeepSeek-R1, which depend on extended chain-of-thought reasoning.
Bayesian Analysis of Interpretable Aging across Thousands of Lithium-ion Battery Cycles
While one or more anode parameters are uniquely identifiable over every cell's lifetime, cathode parameters become identifiable at mid- to end-of-life, indicating measurable resistive growth in the cathode.
OPTICAL: Leveraging Optimal Transport for Contribution Allocation in Dataset Distillation
The demands for increasingly large-scale datasets pose substantial storage and computation challenges to building deep learning models.
Multi-Level Optimal Transport for Universal Cross-Tokenizer Knowledge Distillation on Language Models
Knowledge distillation (KD) has become a prevalent technique for compressing large language models (LLMs).
Systematic Feature Design for Cycle Life Prediction of Lithium-Ion Batteries During Formation
Optimization of the formation step in lithium-ion battery manufacturing is challenging due to limited physical understanding of solid electrolyte interphase formation and the long testing time (~100 days) for cells to reach the end of life.
StreetSurfGS: Scalable Urban Street Surface Reconstruction with Planar-based Gaussian Splatting
Reconstructing urban street scenes is crucial due to its vital role in applications such as autonomous driving and urban planning.
Sinkhorn Distance Minimization for Knowledge Distillation
We propose the Sinkhorn Knowledge Distillation (SinKD) that exploits the Sinkhorn distance to ensure a nuanced and precise assessment of the disparity between teacher and student distributions.
Exploiting GPT-4 Vision for Zero-shot Point Cloud Understanding
In this study, we tackle the challenge of classifying the object category in point clouds, which previous works like PointCLIP struggle to address due to the inherent limitations of the CLIP architecture.
Heredity-aware Child Face Image Generation with Latent Space Disentanglement
The main idea is to disentangle the latent space of a pre-trained generation model and precisely control the face attributes of child images with clear semantics.
Baidu Apollo Auto-Calibration System - An Industry-Level Data-Driven and Learning based Vehicle Longitude Dynamic Calibrating Algorithm
Since manual calibration is not sustainable once entering into mass production stage for industrial purposes, we here introduce a machine-learning based auto-calibration system for autonomous driving vehicles.
