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Results for “BPE” · papers 2 · wiki 2
Academic Papers · 2arXiv q-fin live 1 · desk corpus 1
arXiv · arXiv q-fin · 2026

Unlocking Noisy Real-World Corpora for Foundation Model Pre-Training via Quality-Aware Tokenization

Current tokenization methods process sequential data without accounting for signal quality, limiting their effectiveness on noisy real-world corpora. We present QA-Token (Quality-Aware Tokenization), which incorporates data reliability directly into vocabulary construction. We make three key contributions: (i) a bilevel optimization formulation that jointly optimizes vocabulary construction and downstream performance…

Arvid E. Gollwitzer, Paridhi Latawa, David de Gruijl, Deepak A. Subramanian, Adrián Noriega de la Colina
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arXiv · arXiv · 2026

DeePM: Regime-Robust Deep Learning for Systematic Macro Portfolio Management

We propose DeePM (Deep Portfolio Manager), a structured deep-learning macro portfolio manager trained end-to-end to maximize a robust, risk-adjusted utility. DeePM addresses three fundamental challenges in financial learning: (1) it resolves the asynchronous "ragged filtration" problem via a Directed Delay (Causal Sieve) mechanism that prioritizes causal impulse-response learning over information freshness; (2) it co…

Kieran Wood, Stephen J. Roberts, Stefan Zohren
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Wiki Entities · 2
AI Systems

Byte Pair Encoding

BPE grows a vocabulary by repeatedly merging the most frequent adjacent pairs, starting from characters or bytes, until a target vocab size is reached.

AI Systems

Tokenizer

A tokenizer splits raw text into the discrete tokens a model actually sees — bytes, characters, or learned subwords — and defines the vocabulary the softmax is over.

Option Blackboard · 0
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Encyclopedia · 1
AI Systems · Foundations

Byte Pair Encoding

BPE grows a vocabulary by repeatedly merging the most frequent adjacent pairs, starting from characters or bytes, until a target vocab size is reached.

Cards · 0
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