A vector-quantized autoencoder learns minimal control codebooks for forward invariance in sampled-data control, achieving 157x reduction over grid baselines on a 12D quadrotor model.
Neural discrete representation learning
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 6representative citing papers
ToLL pretrains 3D scene graph generators via anchor-conditioned topological layout recovery and asymmetric structural distillation to learn predicate constraints rather than geometric interpolation shortcuts.
CoLA-Flow Policy encodes action sequences into a continuous latent space and learns an explicit flow there, yielding near-single-step inference with up to 93.7% smoother trajectories and 25-point higher task success than raw-action flow baselines.
Scene-adaptive lattice vector quantization improves rate-distortion performance of 3DGS compression over uniform scalar quantization while adding little overhead and supporting multiple bit rates from one trained model.
MSDformer introduces a multi-scale discrete transformer that tokenizes time series at multiple scales and models them autoregressively in discrete space, claiming superior performance over prior DTM methods with rate-distortion theoretical support.
MTEEG uses task-specific LoRA modules to jointly adapt a pre-trained EEG model across multiple tasks, outperforming single-task baselines on most metrics in evaluations on six downstream tasks.
citing papers explorer
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Minimal Information Control Invariance via Vector Quantization
A vector-quantized autoencoder learns minimal control codebooks for forward invariance in sampled-data control, achieving 157x reduction over grid baselines on a 12D quadrotor model.
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ToLL: Topological Layout Learning with Asymmetric Cross-View Structural Distillation for 3D Scene Graph Generation Pretraining
ToLL pretrains 3D scene graph generators via anchor-conditioned topological layout recovery and asymmetric structural distillation to learn predicate constraints rather than geometric interpolation shortcuts.
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CoLA-Flow Policy: Temporally Coherent Imitation Learning via Continuous Latent Action Flow Matching for Robotic Manipulation
CoLA-Flow Policy encodes action sequences into a continuous latent space and learns an explicit flow there, yielding near-single-step inference with up to 93.7% smoother trajectories and 25-point higher task success than raw-action flow baselines.
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Improving 3D Gaussian Splatting Compression by Scene-Adaptive Lattice Vector Quantization
Scene-adaptive lattice vector quantization improves rate-distortion performance of 3DGS compression over uniform scalar quantization while adding little overhead and supporting multiple bit rates from one trained model.
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MSDformer: Multi-scale Discrete Transformer For Time Series Generation
MSDformer introduces a multi-scale discrete transformer that tokenizes time series at multiple scales and models them autoregressively in discrete space, claiming superior performance over prior DTM methods with rate-distortion theoretical support.
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Towards Unified Multi-task EEG Analysis with Low-Rank Adaptation
MTEEG uses task-specific LoRA modules to jointly adapt a pre-trained EEG model across multiple tasks, outperforming single-task baselines on most metrics in evaluations on six downstream tasks.