14 accepted papers
Sparse Autoencoders (SAEs) have become a cornerstone in mechanistic interpretability. However, current training methods inherit the Block Training paradigm from LLM pre-training. We identify this as a critical methodological oversight when applied to instruct models. Theoretically, utilizing GSNR an…
Spatial Transcriptomics (ST) is a technology that measures gene expression profiles within tissue sections while retaining spatial context. It reveals localized gene expression patterns and tissue heterogeneity, both of which are essential for understanding disease etiology. However, its high cost h
The field of 4D world modeling—aiming to jointly capture spatial geometry and temporal dynamics—has witnessed remarkable progress in recent years, driven by advances in large-scale generative models and multimodal learning. However, the development of truly general 4D world models remains fundamen…
Multitask genetic programming (MTGP) is one of the primary methods for solving multitask symbolic regression (MTSR), the problem of discovering mathematical expressions for multiple interconnected tasks simultaneously. However, conventional MTGP approaches discard a wealth of valuable knowledge from
Dataset distillation creates a small distilled set that enables efficient training by capturing key information from the full dataset. While existing dataset distillation methods perform well on balanced datasets, they struggle under long-tailed distributions, where imbalanced class frequencies indu
Alzheimer’s disease (AD) is an incurable neurodegenerative disease that involves structural changes in the brain. Early diagnosis of AD helps provide timely treatment and delay its progressive process. Many studies have been conducted based on brain images to detect AD. However, these works are most…
Unmanned Aerial Vehicles (UAVs) are widely adopted across various fields, yet they raise significant privacy and safety concerns, demanding robust monitoring solutions. Existing anti-UAV methods primarily focus on position tracking but fail to capture UAV behavior and intent. To address this, we int…
Language models can be steered by modifying their internal representations to control concepts such as emotion, style, or truthfulness in generation. However, the conditions for an effective intervention remain unclear and are often validated through heuristics and trial-and-error. To fill this gap,
We introduce Lumina-Image 2.0, an advanced text-to-image (T2I) model that surpasses previous state-of-the-art methods across multiple benchmarks. Lumina-Image 2.0 is characterized by two key features: (1) Unification - it adopts a unified architecture (Unified Next-DiT) that treats text and image to…
Video generation techniques have made remarkable progress, promising to be the foundation of interactive world exploration. However, existing video generation datasets are not well-suited for world exploration training as they suffer from some limitations: limited locations, short duration, static s…
The relationship between the number of training data points, the number of parameters, and the generalization capabilities of models has been widely studied. Previous work has shown that double descent can occur in the over-parameterized regime and that the standard bias-variance trade-off holds in…
With the increasing attention to large vision-language models such as CLIP, there has been a significant amount of effort dedicated to building efficient prompts. Unlike conventional methods of only learning one single prompt, we propose to learn multiple comprehensive prompts to describe diverse ch…
Mainstream methods for deep metric learning can be divided into pair-based and proxy-based methods. In recent years, proxy-based methods have attracted wide attention for their low training complexity and fast network convergence. Most proxy-based studies assign only one proxy per class to capture t…
Metric learning is a widely-used method for image retrieval. The object of metric learning is to limit the distance between similar samples and increase the distance between samples of different classes through learning. Many studies tend to pay more attention to keep the distance between positive a…