Posters

Spiking neural network effectively segments mixed-motion event streams via spike-based Bayesian computation, achieving efficient real-time motion decoupling.

CITRUS: a novel continuous graph neural network efficiently processes multidomain data on multiple graphs, achieving superior performance in spatiotemporal forecasting.

CutSSL: a novel framework for graph-based semi-supervised learning, surpasses state-of-the-art accuracy by solving a continuous nonconvex quadratic program that provably yields integer solutions, exce…

NerPE: continuous heatmap regression via implicit neural representation resolves the accuracy-limiting quantization errors in human pose estimation, achieving sub-pixel precision.

CCL, a novel probabilistic framework, uses continuous contrastive learning to excel in long-tailed semi-supervised recognition, surpassing prior state-of-the-art methods by over 4%.

Researchers developed a novel continual learning method achieving state-of-the-art performance by aligning data representations across tasks using pre-trained tokens, eliminating the need for experien…

Boost continual learning efficiency with CLFD: a novel frequency domain approach that improves accuracy by up to 6.83% and slashes training time by 2.6x on edge devices!

Continual counting with gradual privacy expiration: A new algorithm achieves optimal accuracy with exponentially decaying privacy!

ContAV-Sep: a novel approach to continual audio-visual sound separation, effectively mitigating catastrophic forgetting and improving model adaptability by preserving cross-modal semantic similarity a…

Contextual MNL bandits are revolutionized with general value functions, offering enhanced algorithms for stochastic and adversarial settings, surpassing previous results in accuracy and efficiency.

This paper introduces induced empirical risk minimization for contextual linear optimization with bandit feedback, providing theoretical guarantees and computationally tractable solutions for improved…

This work unveils a novel algorithm for contextual decision-making with knapsacks, achieving significantly improved regret bounds beyond worst-case scenarios, thereby offering a more practical and eff…

Contextual Bilevel Reinforcement Learning (CB-RL) tackles real-world strategic decision-making where optimal policies depend on environmental configurations and exogenous events, proposing a stochasti…

CAMS, a novel contextual active model selection algorithm, minimizes labeling costs by strategically selecting pre-trained models and querying labels for data points, achieving significant improvement…

ContextGS: Revolutionizing 3D scene compression with an anchor-level autoregressive model, achieving 15x size reduction in 3D Gaussian Splatting while boosting rendering quality.

CONTEXTCITE pinpoints which parts of a given context led a language model to generate a specific statement, improving model verification and response quality.

Context-Aware Testing (CAT) revolutionizes ML model testing by using contextual information to identify relevant failures, surpassing traditional data-only methods.

Novel implicit field representation accurately reconstructs multi-person interaction geometry in 3D, simultaneously capturing occupancy, instance IDs, and contact fields, surpassing existing methods.

Researchers created a Materials Knowledge Graph (MKG) using large language models to efficiently organize and integrate knowledge from a decade of high-quality materials science research, enhancing da…

Researchers developed semantics-aware adversarial examples using a probabilistic approach, achieving higher success rates in bypassing defenses while remaining undetectable to humans.