arXiv subjects

Jiang Ji

Publications and source records attributed to Jiang Ji.

3 recordsLinked to original sources

arXiv2026

What Transfers from Training-Only Priors? Separating Knowledge, Ordinal Targets, and Optimization

Training-only teacher access lets a compact student deploy with local features and exported weights. This established distillation interface does not identify what transfers or when it helps. We separate label, query, capacity, and source-access budgets on three public credit archives and a previously unexamined bankruptcy archive. Shared tree parameters, shuffles, and optimizer interventions expose limitations of historical Ranking Prior Alignment; no tested single repair consistently rescues it. Heldout source queries and matched warmup and mappings retain a rank-listwise advantage over mapped staged KD in 24 credit configuration means and all six teacher--capacity means of a frozen new-archive test, with explicit query failures and stronger source-weight delivery under a different interface. A pretrained TabICLv2 follow-up at 100 labels initially improves matched supervision in only one of eight means; six of nine source-predicted policy gains reverse on target. A registered seen-archive intervention adds 500 or 2,000 unlabeled queries: all eight matched AUC means become positive under selected and fixed alpha. However, outer effects are mixed and no conditional paired-draw interval for these supervised contrasts lies wholly above zero. Increasing teacher context at 500 student labels does not beat matched supervision in any of eight ordinary-context means, despite stronger direct teacher performance. Rank KD retains substantial probability errors. Independent reloads verify zero guarded external inference accesses. Results support query/context boundary diagnostics, without universal superiority, population significance, a prospective transfer rule, or an LLM-specific advantage.

cs.LG↗

arXiv2026

VR-JEPA: Learning Contrastive-State Latent Guidance for Generation-based Video Reasoning

Reasoning through video generation offers a promising path toward visual intelligence by modeling latent visual states and their dynamics. However, current video generation models often lack explicit guidance on how these states should evolve, leaving generated trajectories prone to physical and structural inconsistencies that undermine reasoning reliability. While the Video Joint-Embedding Predictive Architecture (V-JEPA) provides rich spatiotemporal priors learned through latent prediction, these general priors do not naturally adapt to the logical reasoning capabilities required for complex visual tasks. To bridge this gap, we propose VR-JEPA, a framework that aligns the V-JEPA predictor with task-specific reasoning logic through localized contrastive-state learning and uses its predicted latent trajectories to guide video generation for visual reasoning. Specifically, (i) we pair successful trajectories with generated alternatives under the same input conditions and use discrepancies in their V-JEPA representations to identify informative states and tokens for localized contrastive supervision. (ii) We further equip the V-JEPA predictor with skill-specific experts trained on anchor-task data, allowing the model to adaptively specialize its shared spatiotemporal priors across diverse cognitive domains. Together with skill-specific experts, this contrastive supervision enables VR-JEPA to predict latent trajectories that provide task-specific logical guidance for video generation. Comprehensive experiments on the large-scale VBVR-Pro-Bench dataset demonstrate that VR-JEPA achieves an $11.33\%$ relative improvement over the cutting-edge generation-based reasoning baseline, significantly mitigating physical artifacts and enhancing logical consistency.

cs.CV↗

arXiv2018

Deploy Large-Scale Deep Neural Networks in Resource Constrained IoT Devices with Local Quantization Region

Implementing large-scale deep neural networks with high computational complexity on low-cost IoT devices may inevitably be constrained by limited computation resource, making the devices hard to respond in real-time. This disjunction makes the state-of-art deep learning algorithms, i.e. CNN (Convolutional Neural Networks), incompatible with IoT world. We present a low-bit (range from 8-bit to 1-bit) scheme with our local quantization region algorithm. We use models in Caffe model zoo as our example tasks to evaluate the effect of our low precision data representation scheme. With the available of local quantization region, we find implementations on top of those schemes could greatly retain the model accuracy, besides the reduction of computational complexity. For example, our 8-bit scheme has no drops on top-1 and top-5 accuracy with 2x speedup on Intel Edison IoT platform. Implementations based on our 4-bit, 2-bit or 1-bit scheme are also applicable to IoT devices with advances of low computational complexity. For example, the drop on our task is only 0.7% when using 2-bit scheme, a scheme which could largely save transistors. Making low-bit scheme usable here opens a new door for further optimization on commodity IoT controller, i.e. extra speed-up could be achieved by replacing multiply-accumulate operations with the proposed table look-up operations. The whole study offers a new approach to relief the challenge of bring advanced deep learning algorithm to resource constrained low-cost IoT device.

cs.LG↗