Stochastic Augmentation
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Related Articles from SNS
AugMask: Training Diffusion Models on Incomplete Tabular Data via Stochastic Augmentation and Masking
arXiv:2606.03347v1 Announce Type: new Abstract: Score-based diffusion models have emerged as prominent deep generative models; however, their application to tabular data remains challenging because their backbones assume fully specified inputs, whereas real-world tabular data often contain missing values. We propose AugMask, a plug-and-play training framework that adapts missing-unaware backbones to incomplete data by separating conditioning from supervision. AugMask 1) constructs numeric...
RADE: Random Add-Drop Edge as a Regularizer
arXiv:2606.00757v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) suffer from overfitting and over-squashing of long-range information. Stochastic graph augmentations (e.g., edge deletion) regularize training against overfitting but can introduce train-inference misalignment and do not improve over-squashing. In contrast, rewiring methods improve connectivity to mitigate over-squashing, but are not designed to regularize training.
Magenta RealTime 2: Open and Local Live Music Models
We’re excited to share Magenta RealTime 2 (MRT2), a state-of-the-art open model and efficient real-time inference engine that enables you to build and play AI musical instruments on your laptop! To get started, download the apps on your MacBook (requires Apple Silicon). Unlike other large generative music models that work offline to turn a prompt into a track, MRT2 is a live, interactive model that you can control with MIDI and audio, in addition to text.
Residual-Controlled Multiplier Learning for Stochastic Constrained Decision-Making
arXiv:2606.07088v1 Announce Type: new Abstract: Stochastic constrained decision-making requires optimizing performance objectives while enforcing statistical requirements such as safety or fairness. However, standard primal--dual methods struggle to update multipliers robustly under stochastic mini-batch feedback, as the noise of mini-batch gradients and constraint estimates can be directly accumulated into the multiplier memory.
Closing the Prior-Posterior Loop: Self-Reflective Molecular Design with Analysis-Driven LLM Iteration
arXiv:2606.09520v1 Announce Type: new Abstract: Can a general-purpose large language model design molecules with the precision of a seasoned chemist? Current LLM-based frameworks answer this question with scalar feedback loops-generate, score, reject-that amount to informed trial-and-error. Here we show that replacing a single number with the full physicochemical rationale from first-principles calculations transforms the LLM from a stochastic sampler into a causal reasoner.
MB-Loc: Multi-planar Bird's-eye-view Localization in outdoor LiDAR scenes
Announce Type: new Abstract: Global LiDAR localization is a fundamental task for autonomous navigation systems. Recent methods perform Scene Coordinate Regression (SCR) and achieve superior accuracy over Absolute Pose Regression (APR) solutions by predicting dense 3D world coordinates. However, SCR approaches introduce two major bottlenecks: severe computational inefficiency from processing raw 3D geometries and significant performance degradation under varying sensor viewpoints.
Closing the Prior-Posterior Loop: Self-Reflective Molecular Design with Analysis-Driven LLM Iteration
arXiv:2606.09520v1 Announce Type: cross Abstract: Can a general-purpose large language model design molecules with the precision of a seasoned chemist? Current LLM-based frameworks answer this question with scalar feedback loops-generate, score, reject-that amount to informed trial-and-error. Here we show that replacing a single number with the full physicochemical rationale from first-principles calculations transforms the LLM from a stochastic sampler into a causal reasoner.
Mitigating Proxy-to-Wild Domain Gap in Deepfake Speech
arXiv:2606.07494v1 Announce Type: new Abstract: Recent neural audio codec-based speech generation (CodecFake) produces highly realistic audio, posing a challenge to existing deepfake countermeasure models. While using codec resynthesized speech (CoRS) as proxy data improves performance, it often suffers from limited generalization.
Distributional Approximate Nearest Neighbour Search for Uncertainty-Aware Retrieval
Announce Type: new Abstract: Approximate Nearest Neighbour search indices form the backbone of real-world recommender systems, enabling real-time candidate retrieval over million-item catalogues. Typically, a single point estimate embedding is learnt for every user and every item. At serving time, the user embedding queries the index for relevant items.
Diffusion Models for Hyperspectral Image Analysis: A Comprehensive Review
Announce Type: replace-cross Abstract: Hyperspectral image (HSI) analysis plays a critical role in remote sensing, agriculture, and environmental monitoring. However, traditional methods often struggle to handle the high dimensionality, spectral redundancy, and noise inherent in HSI data, limiting their accuracy and scalability. Recently, diffusion models including denoising diffusion probabilistic models and other generative frameworks based on stochastic differential equations have shown...