Search Results for author: Matthew Dixon

Found 9 papers, 2 papers with code

On the Abuse and Detection of Polyglot Files

no code implementations1 Jul 2024 Luke Koch, Sean Oesch, Amul Chaulagain, Jared Dixon, Matthew Dixon, Mike Huettal, Amir Sadovnik, Cory Watson, Brian Weber, Jacob Hartman, Richard Patulski

In this work we found that existing file-format and embedded-file detection tools, even those developed specifically for polyglot files, fail to reliably detect polyglot files used in the wild, leaving organizations vulnerable to attack.

Malware Detection

Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

no code implementations22 Apr 2024 Marah Abdin, Jyoti Aneja, Hany Awadalla, Ahmed Awadallah, Ammar Ahmad Awan, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Jianmin Bao, Harkirat Behl, Alon Benhaim, Misha Bilenko, Johan Bjorck, Sébastien Bubeck, Martin Cai, Qin Cai, Vishrav Chaudhary, Dong Chen, Dongdong Chen, Weizhu Chen, Yen-Chun Chen, Yi-Ling Chen, Hao Cheng, Parul Chopra, Xiyang Dai, Matthew Dixon, Ronen Eldan, Victor Fragoso, Jianfeng Gao, Mei Gao, Min Gao, Amit Garg, Allie Del Giorno, Abhishek Goswami, Suriya Gunasekar, Emman Haider, Junheng Hao, Russell J. Hewett, Wenxiang Hu, Jamie Huynh, Dan Iter, Sam Ade Jacobs, Mojan Javaheripi, Xin Jin, Nikos Karampatziakis, Piero Kauffmann, Mahoud Khademi, Dongwoo Kim, Young Jin Kim, Lev Kurilenko, James R. Lee, Yin Tat Lee, Yuanzhi Li, Yunsheng Li, Chen Liang, Lars Liden, Xihui Lin, Zeqi Lin, Ce Liu, Liyuan Liu, Mengchen Liu, Weishung Liu, Xiaodong Liu, Chong Luo, Piyush Madan, Ali Mahmoudzadeh, David Majercak, Matt Mazzola, Caio César Teodoro Mendes, Arindam Mitra, Hardik Modi, Anh Nguyen, Brandon Norick, Barun Patra, Daniel Perez-Becker, Thomas Portet, Reid Pryzant, Heyang Qin, Marko Radmilac, Liliang Ren, Gustavo de Rosa, Corby Rosset, Sambudha Roy, Olatunji Ruwase, Olli Saarikivi, Amin Saied, Adil Salim, Michael Santacroce, Shital Shah, Ning Shang, Hiteshi Sharma, Yelong Shen, Swadheen Shukla, Xia Song, Masahiro Tanaka, Andrea Tupini, Praneetha Vaddamanu, Chunyu Wang, Guanhua Wang, Lijuan Wang, Shuohang Wang, Xin Wang, Yu Wang, Rachel Ward, Wen Wen, Philipp Witte, Haiping Wu, Xiaoxia Wu, Michael Wyatt, Bin Xiao, Can Xu, Jiahang Xu, Weijian Xu, Jilong Xue, Sonali Yadav, Fan Yang, Jianwei Yang, Yifan Yang, ZiYi Yang, Donghan Yu, Lu Yuan, Chenruidong Zhang, Cyril Zhang, Jianwen Zhang, Li Lyna Zhang, Yi Zhang, Yue Zhang, Yunan Zhang, Xiren Zhou

We introduce phi-3-mini, a 3. 8 billion parameter language model trained on 3. 3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3. 5 (e. g., phi-3-mini achieves 69% on MMLU and 8. 38 on MT-bench), despite being small enough to be deployed on a phone.

Ranked #5 on MMR total on MRR-Benchmark (using extra training data)

Language Modelling Math +2

Beyond Surrogate Modeling: Learning the Local Volatility Via Shape Constraints

1 code implementation20 Dec 2022 Marc Chataigner, Areski Cousin, Stéphane Crépey, Matthew Dixon, Djibril Gueye

We explore the abilities of two machine learning approaches for no-arbitrage interpolation of European vanilla option prices, which jointly yield the corresponding local volatility surface: a finite dimensional Gaussian process (GP) regression approach under no-arbitrage constraints based on prices, and a neural net (NN) approach with penalization of arbitrages based on implied volatilities.

MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather

no code implementations18 Nov 2021 Sylwester Klocek, Haiyu Dong, Matthew Dixon, Panashe Kanengoni, Najeeb Kazmi, Pete Luferenko, Zhongjian Lv, Shikhar Sharma, Jonathan Weyn, Siqi Xiang

We present the encoder-forecaster convolutional long short-term memory (LSTM) deep-learning model that powers Microsoft Weather's operational precipitation nowcasting product.

Optical Flow Estimation

Deep Local Volatility

1 code implementation20 Jul 2020 Marc Chataigner, Stéphane Crépey, Matthew Dixon

Deep learning for option pricing has emerged as a novel methodology for fast computations with applications in calibration and computation of Greeks.

Deep Learning Experimental Design

G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning

no code implementations25 Feb 2020 Matthew Dixon, Igor Halperin

Our approach is based on G-learning - a probabilistic extension of the Q-learning method of reinforcement learning.

Management Q-Learning +3

OSTSC: Over Sampling for Time Series Classification in R

no code implementations27 Nov 2017 Matthew Dixon, Diego Klabjan, Lan Wei

The OSTSC package is a powerful oversampling approach for classifying univariant, but multinomial time series data in R. This article provides a brief overview of the oversampling methodology implemented by the package.

Classification General Classification +3

Classification-based Financial Markets Prediction using Deep Neural Networks

no code implementations29 Mar 2016 Matthew Dixon, Diego Klabjan, Jin Hoon Bang

Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers.

Algorithmic Trading Classification +1

Cannot find the paper you are looking for? You can Submit a new open access paper.