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Found 8 papers, 2 papers with code
ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search
Retrieval-based code question answering seeks to match user queries in natural language to relevant code snippets.
Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networks
The ability to construct a realistic simulator of financial exchanges, including reproducing the dynamics of the limit order book, can give insight into many counterfactual scenarios, such as a flash crash, a margin call, or changes in macroeconomic outlook.
Towards systematic intraday news screening: a liquidity-focused approach
Given the huge amount of news articles published each day, most of which are neutral, we present a systematic news screening method to identify the ``true'' impactful ones, aiming for more effective development of news sentiment learning methods.
Multi-asset market making under the quadratic rough Heston
Given the promising results on joint modeling of SPX/VIX smiles of the recently introduced quadratic rough Heston model, we consider a multi-asset market making problem on SPX and its derivatives, e. g. VIX futures, SPX and VIX options.
Improving Variational Autoencoders with Density Gap-based Regularization
There are effective ways proposed to prevent posterior collapse in VAEs, but we observe that they in essence make trade-offs between posterior collapse and hole problem, i. e., mismatch between the aggregated posterior distribution and the prior distribution.
On the universality of the volatility formation process: when machine learning and rough volatility agree
We train an LSTM network based on a pooled dataset made of hundreds of liquid stocks aiming to forecast the next daily realized volatility for all stocks.
Enhancing Dual-Encoders with Question and Answer Cross-Embeddings for Answer Retrieval
To keep independent encoding of questions and answers during inference stage, variational auto-encoder is further introduced to reconstruct answers (questions) from question (answer) embeddings as an auxiliary task to enhance QA interaction in representation learning in training stage.
Deep calibration of the quadratic rough Heston model
The quadratic rough Heston model provides a natural way to encode Zumbach effect in the rough volatility paradigm.
