On-device research index

arXiv research

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.

168,695 papers · 148 categories

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9 results for “fixed-precision”

Ranking-and-Selection with Multiple Correct Answers and Non-Answerable Estimatescs.LG

Unified framework for ranking-and-selection with multiple correct answers and non-answerable estimates

problem Fixed-precision ranking-and-selection in structured settings with non-unique answers and non-answerable estimates

method Unified framework based on answer-wise acceptance sets, restricted generalized likelihood ratio stopping, and answer-pitfall decomposition

result Unified recipe performs well across a broad range of pure-exploration problems

ranking-and-selection structured settings non-unique answers non-answerable estimates fixed-precision

2026-06-20abs ↗pdf ↗

Efficient Model-Based Collaborative Filtering with Fast Adaptive PCAcs.LG

Efficient CF approach using fast adaptive PCA for recommender systems.

problem Matrix completion problem in recommender systems.

method Fast adaptive randomized singular value decomposition (SVD) and termination mechanism for latent factors.

result The approach achieves near optimal prediction accuracy with high runtime efficiency.

collaborative filtering matrix completion recommender systems fast PCA randomized SVD

2020-09-04abs ↗pdf ↗

Analyzing the Power of Chain of Thought through Memorization Capabilitiescs.LG

Transformers with CoT don't enhance reasoning power across all tasks.

problem Does CoT enhance the reasoning power of transformers?

method Examined the memorization capabilities of fixed-precision transformers with and without CoT.

result Transformers with CoT cannot memorize all reasoning tasks, leading to a negative answer.

transformers chain of thought memorization reasoning mathematical reasoning

2025-11-03abs ↗pdf ↗

Gaussian meta-embeddings for efficient scoring of a heavy-tailed PLDA modelstat.ML

Embeddings in machine learning are low-dimensional representations of complex input patterns, with the property that simple geometric operations like Euclidean distances and dot products can be used for classification and comparison tasks. The proposed meta-embeddings are special embeddings that live in more general in…

2018-02-27abs ↗pdf ↗

Ultrafast On-Chip Online Learning via Spline Locality in Kolmogorov-Arnold Networkscs.AR

Kolmogorov-Arnold Networks enable ultrafast online learning with fixed-point quantization.

problem Efficient online learning for high-frequency systems with strict memory constraints.

method Fixed-point online training on FPGAs exploiting B-spline locality in KANs.

result Kolmogorov-Arnold Networks are more efficient and expressive than MLPs for low-latency tasks.

online learning Kolmogorov-Arnold Networks fixed-point quantization sub-microsecond latency FPGAs

2026-02-02abs ↗pdf ↗

Exact Reformulation and Optimization for Direct Metric Optimization in Binary Imbalanced Classificationcs.LG

Paper tackles imbalanced binary classification by optimizing precision and recall directly.

problem Imbalanced binary classification where standard accuracy is misleading.

method Exact constrained reformulations for precision and recall optimization.

result ERO framework outperforms state-of-the-art methods on multiple datasets.

imbalanced classification binary classification precision recall direct metric optimization

2025-07-21abs ↗pdf ↗

CoDeQ: End-to-End Joint Model Compression with Dead-Zone Quantizer for High-Sparsity and Low-Precision Networkscs.LG

CoDeQ simplifies joint model compression by integrating pruning and quantization.

problem Joint pruning and quantization methods are complex and require additional procedures.

method CoDeQ uses a dead-zone quantizer to directly induce sparsity and learn quantization parameters.

result CoDeQ achieves high sparsity and low-precision accuracy with minimal bit operations.

model compression joint pruning quantization dead-zone sparsity

2025-12-15abs ↗pdf ↗

Quantum algorithms for Second-Order Cone Programming and Support Vector Machinesquant-ph

We present a quantum interior-point method (IPM) for second-order cone programming (SOCP) that runs in time $\widetilde{O} \left( n\sqrt{r} \frac{ζκ}{δ^2} \log \left(1/ε\right) \right)$ where $r$ is the rank and $n$ the dimension of the SOCP, $δ$ bounds the distance of intermediate solutions from the cone boundary, $ζ$ …

2019-08-19abs ↗pdf ↗

Transformers are Universal In-context Learnerscs.CL

Transformers can predict new tokens based on any number of context tokens, approximating continuous mappings with fixed resources.

problem Handling an arbitrarily large number of context tokens in transformers.

method Mathematical analysis of transformer's expressivity using Wasserstein distance and continuous mappings.

result Deep transformers are universal and can approximate continuous in-context mappings to arbitrary precision, uniformly over compact token domains.

transformers in-context learning universal approximation Wasserstein distance MLP

2024-08-02abs ↗pdf ↗