VTA offers flexible DL specialization for evolving workloads.
problem Inflexible specialized DL hardware accelerators.
method Parametrizable architecture, two-level ISA, JIT compiler.
result Flexible deep learning specialization on edge-class FPGAs.
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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
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VTA combines verbal and latent reasoning for accurate stock time-series forecasts.
problem Challenges in combining textual analysis with time-series data for financial forecasting.
method Converts stock price data into textual annotations, optimizes reasoning trace using inverse MSE, conditions time-series model outputs on reasoning attributes.
result VTA achieves state-of-the-art forecasting accuracy and interpretable reasoning traces.
VTA learns hierarchical temporal structure for sequential data.
problem Learning interpretable temporal structure in sequential data.
method Hierarchical recurrent state space model with variational approach.
result VTA models 2D and 3D visual sequences with hierarchical structure.