arXiv Machine Learning
MaskCode: Mask Transformer for Feedback-Assisted Coding With Linear Block Codes
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arXiv Machine Learning
Sep 22
Scalable Mamba-Based Message-Passing Neural Decoder for Error-Correcting Codes
arXiv:2605.10681v2 Announce Type: replace-cross Abstract: Forward error correction is essential for reliable communication over noisy channels. Attention-based model-free neural decoders have shown s...
arXiv Machine Learning
Aug 28
Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding
The paper demonstrates that incorporating code automorphisms into syndrome-based neural decoding (SBND) improves the models’ learning and generalization through data augmentation during training and inference. By applying this technique to short, high-rate codes, the authors achieve performance close to maximum likelihood decoding (MLD) using small datasets and appropriate training. The study also indicates that previous SBND results may have underestimated their true error‑correction capability due to insufficient training.
arXiv Computer Vision
Sep 7
Scalable Neural Video Representation Compression
Scalable Neural Video Representation Compression (S-NVRC) introduces a scalable implicit neural representation (INR) video codec that supports fine-grained bitrate and decoding‑complexity scalability from a single embedded bitstream. It uses a coarse‑to‑fine prefix for feature grids and a nested prefix for network layers, enabling a wide range of operating points while maintaining a single encoding. On the UVG dataset, S‑NVRC outperforms SHM 12.4 and multi‑layer VTM‑20.0 by 43.7 % and 5.6 % in BD‑rate, respectively, and offers flexible complexity scalability.
arXiv Machine Learning
Jul 31
WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning
arXiv:2607. 28418v1 Announce Type: cross Abstract: Pruning is a promising approach for improving the efficiency of LLMs.
arXiv AI
Jul 10
Linear Attention Architectures: Mechanisms, Trade-offs, and Cross-Layer Routing
arXiv:2607. 07953v1 Announce Type: cross Abstract: Self-attention lets each token retrieve information from the full context, but its quadratic cost in sequence length limits training and inference at long context.
arXiv Computer Vision
Sep 23
StableVQ: Practical Guidelines for Stable Vector-Quantized Tokenizer Training
StableVQ introduces practical guidelines to improve training stability for vector‑quantized tokenizers used in image generation models. It addresses instability caused by the entanglement of encoder–decoder and codebook training by proposing three techniques: Dynamic STE for the encoder, Region VQ Loss for the codebook, and a Decoupled Schedule for independent learning rates. Experiments on ImageNet show consistent gains in stability, codebook utilization, and reconstruction quality across various settings.