Quantization, distillation, pruning and serving work aimed at the same accuracy for less memory, latency and money.
arXiv:2609.06271v1 Announce Type: cross Abstract: Some low-cost Internet of Things (IoT) sensor deployments lack device-level source authentication, leaving them vulnerable to impersonation or inject...
The paper introduces NIO Bench, a benchmarking framework that profiles storage I/O for six machine learning model types, using Python hooks and Linux strace to capture detailed access patterns. Experiments on a Ceph-backed Kubernetes cluster show that I/O is dominated by data preparation, model loading, and checkpointing, with training becoming compute-bound once data is staged. The study finds a power‑law distribution of file usage and identifies cache‑miss read tail latency as the main storage bottleneck, recommending aggressive prefetching, page‑cache pinning, and bursty write handling for ML‑optimized storage.
arXiv:2609.08759v1 Announce Type: cross Abstract: Quantization schemes based on randomized rotations have recently received renewed attention, including the roles of MMSE and unbiased reconstruction...
The paper presents TASTE, a method that uses Bayesian optimization to tune batch size for on‑device edge learning, aiming to maximize hardware throughput while preserving accuracy. Experiments on devices like the Raspberry Pi 4 show that the tuned batch size, combined with gradient accumulation and linear learning‑rate scaling, can double training throughput compared to using the maximum batch size. In online continual learning, the optimal batch size also helps balance stability and plasticity, reducing catastrophic forgetting without sacrificing efficiency.
arXiv:2609.08638v1 Announce Type: cross Abstract: An action chunk can span several stages of a manipulation task, yet a label for its first step describes only the current stage. We introduce Chunk-A...
arXiv:2609.07410v2 Announce Type: cross Abstract: Deformability cytometry (DC) is a type of imaging flow cytometry, which uses a camera-equipped device to measure cellular stiffness in addition to ot...
IXPLORE is a bounded ideal point estimation algorithm that blends a predictive fit objective with a sparsity‑aware likelihood function. It outperforms both traditional model‑based methods like IRT and other machine‑learning approaches on reconstruction and imputation error across five benchmark datasets, especially for users with sparse responses. The method also incorporates non‑linear feature transforms for further error reduction while maintaining visual interpretability, and it quantifies uncertainty via grid‑based posterior inference on a bounded 2D latent space.
arXiv:2101.02307v4 Announce Type: replace-cross Abstract: Mixed membership modeling for undirected networks has been extensively explored in network science over the past few years. Despite the subst...
The paper demonstrates that image knowledge distillation can be backdoored even when the teacher model is clean, by poisoning the distillation dataset with triggered and manipulated images that the teacher already classifies as a target label. The attack, effective at poisoning rates as low as 10%, uses targeted adversarial perturbations and GAN-based class transitions to embed a backdoor into the student model while preserving its performance on clean data. The study highlights that the security of knowledge distillation depends not only on the teacher but also on the integrity of the distillation data.
arXiv:2609.10305v1 Announce Type: new Abstract: Language models under one million parameters matter for edge deployment, domain adaptation, and reproducible research, yet a two-layer LSTM or Transfor...
The paper introduces a reasoning‑aware compression framework for Large Reasoning Models (LRMs) that identifies vulnerable reasoning circuits and selectively restores them to FP16 to avoid performance loss. By benchmarking INT4 quantization across five reasoning tasks (GSM8K, FOLIO, MATH‑500, ProofWriter, MuSiQue) and measuring GPU energy, the authors find that uniform quantization can actually increase energy consumption and that vulnerability varies by task and architecture. Selective compression yields Pareto‑optimal trade‑offs, achieving significant energy savings while improving or matching accuracy on held‑out data.
arXiv:2609.10346v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) process hundreds or thousands of visual tokens per image, incurring prohibitive inference costs. While existin...
arXiv:2509.01809v2 Announce Type: replace-cross Abstract: We consider the problem of support recovery for sparse binary signals from noisy linear measurements. For sparse Gaussian measurement matrice...
arXiv:2609.07762v1 Announce Type: cross Abstract: Recurrent detectors such as bidirectional long short-term memory (Bi-LSTM) networks are low-complexity alternatives to the optimal Bahl-Cocke-Jelinek...
arXiv:2609.09924v1 Announce Type: new Abstract: Emotion Recognition in Conversations (ERC) requires integrating heterogeneous textual, audio, and visual cues while accounting for conversational conte...
The paper introduces TokCode, a token encoding framework that enhances robustness in generative semantic communication by restructuring redundancy in the semantic domain. TokCode leverages a lightweight adapter to transform a large language model into a token encoder, avoiding the need for a dedicated deep model. A channel-quality-aware distillation method (CADET) trains the adapter across diverse erasure rates, producing a reconfigurable low‑rank adapter that enables efficient reinforcement learning and achieves significant improvements in image similarity over existing receiver‑side recovery benchmarks.
arXiv:2609.08084v1 Announce Type: cross Abstract: Monocular depth estimation is a ubiquitous yet highly ill-posed computer vision task, with downstream applications in scene reconstruction, computati...
arXiv:2609.09863v1 Announce Type: new Abstract: Choosing a deep learning architecture for label-free single-cell classification remains an open question, with microscopy benchmarks reporting conflict...
arXiv:2609.07236v1 Announce Type: cross Abstract: Selecting a parallelism strategy - the configuration of data, tensor, and pipeline parallelism degrees together with micro- and global-batch sizes -...
arXiv:2609.05641v1 Announce Type: cross Abstract: We consider the problem of minimizing error in quantized matrix multiplication $C=AB$. Scalar quantization of the factors introduces rounding errors...