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#数据/训练

Today 0 items
Oct 15Thu
  1. Semiconductor Engineering Chips and Packaging46

    Characterization of HBM Power Consumption (ETH Zurich, NYU)

    Researchers from ETH Zurich and NYU published a paper presenting the first comprehensive experimental characterization of power consumption on real HBM2 DRAM chips, with 13 new findings and 6 key conclusions. Read operations draw significantly more current than other operations, and flipping all bank group bus signals increases power by 29.4% over holding them constant. Based on this, the team constructed access patterns that on average consume 23.6% more power than the JEDEC-specified maximum power measurement loop across 36 tested HBM2 chips.

Oct 13Tue
  2. Microsoft Research40

    Simulating student learning for better AI tutoring

    Microsoft Research released StudentSim, a research system for evaluating and improving AI tutoring tools before testing with real learners, training a personalized simulator for each student based on publicly available de-identified learner records. It also introduces the StudentSimEval benchmark, which evaluates along two dimensions, behavioral fidelity and instructional responsiveness, across three domains: chess, second-language English writing, and math, outperforming leading baselines. The system is for research-stage use only, is not a product, has not been deployed in classrooms, and is not intended to replace teachers.

Sep 11Fri
Aug 28Fri
  1. Google Research50

    Planetary prediction engine: Automating global models via Earth AI

    Google Earth AI has introduced an experimental research capability called the Planetary Prediction Engine (PPE), which autonomously performs the full geospatial prediction workflow—data discovery, cleaning, feature engineering, model training, and evaluation—starting from natural language queries, compressing work that previously took weeks of manual data engineering down to a few minutes.

Aug 25Tue
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Jul 26Sun
  1. Berkeley AI Research46

    Teaching LLMs to Update Beliefs for Efficient Long-Horizon Interaction

    Berkeley AI Research proposes the ABBEL framework, which replaces full interaction history with a natural-language belief state as the agent's working context and introduces belief grading to supervise summary content. On the CollabBench collaborative coding benchmark, ABBEL-rec-BG closes roughly 50% of the performance gap with full-context models, cuts training steps from 100 to 50, and uses significantly shorter peak context token lengths than the full-context setting.

Jul 9Thu
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Jan 10Sat
  1. Berkeley AI Research45

    Information-Driven Design of Imaging Systems

    Berkeley AI Research proposes a NeurIPS 2025 framework that evaluates and optimizes imaging systems directly on information content, using a mutual information metric to unify factors like resolution and noise. The method needs only noise measurements and a noise model to estimate information, and provides an upper bound on true information. Across color photography, radio astronomy, lensless imaging, and microscopy, the metric predicts decoder performance and its optimized results match SOTA end-to-end methods with lower memory and compute and no task-specific decoder.

Nov 1Sat
  1. Berkeley AI Research46

    RL without TD learning

    Berkeley AI Research proposes an off-policy RL value-learning algorithm based on divide and conquer that no longer uses temporal difference (TD) learning, whose Bellman recursion error accumulation makes it hard to scale to long-horizon tasks.

Sep 1Mon
  1. Berkeley AI Research49

    What exactly does word2vec learn?

    Berkeley AI Research proposes a quantitative theory of the word2vec learning process: under several mild approximations, its learning problem reduces to unweighted least-squares matrix factorization, the gradient flow dynamics have a closed-form solution, and the final embeddings are equivalent to PCA on the target matrix M*.