Pinned Repositories
Importantizer
DeveloperWeek 2017 Hackathon
advanced-operating-systems
Adanced OS project, based on Barrelfish
AFLplusplus
AFLplusplus for AST
alexalight
Let there be light! :bulb:
amazonecho
Alexaskills made simple for ruby!
AST
AvaCare
Healthcare made easy with AvaCare!
bit-parallel-database-queries
high-performance SQL queries on bit-parallel & in memory database layouts
fuzzbench
AST fork
lambdapure
bachelor thesis: SSA IR for strict functional language
mattweingarten's Repositories
mattweingarten/alexalight
Let there be light! :bulb:
mattweingarten/amazonecho
Alexaskills made simple for ruby!
mattweingarten/lambdapure
bachelor thesis: SSA IR for strict functional language
mattweingarten/advanced-operating-systems
Adanced OS project, based on Barrelfish
mattweingarten/AFLplusplus
AFLplusplus for AST
mattweingarten/AST
mattweingarten/AvaCare
Healthcare made easy with AvaCare!
mattweingarten/fuzzbench
AST fork
mattweingarten/Hillary-vs-Trump-Game
mattweingarten/bit-parallel-database-queries
high-performance SQL queries on bit-parallel & in memory database layouts
mattweingarten/avacarebackend
mattweingarten/bigdata-exercises
Exercises for the Big Data lecture at ETH Zurich (Fall 2021)
mattweingarten/compilerOptimzationsFuzzing
The automated software testing technique fuzzing has seen a golden age in the last decade, with widespread use in industry and academia. On the hunt to find vulnerabilities, fuzzing binaries are compiled with default compiler optimizations such as -O2, or -O3, which remain the hard-coded default in popular fuzzers such as AFL++. On a binary level, software compiled from the same source code may vastly differ in control flow depending on used compilation flags. In this work, we aim to analyze the impact of different compiler optimizations on the fuzzing process and provide further insight. We influence compilation passes of the clang/LLVM compiler and analyze their impact on the fuzzing performance of AFL++. We integrate our work into Fuzzbench, an open-source fuzzing pipeline, and run experiments on real-world benchmarks. Our preliminary fuzzing results indicate that there is a delicate trade-off between runtime performance and code complexity. While our results show significant differences on the scale of individual benchmarks, when summarizing across the whole bench suite, there is no evidence to suggest a statistical difference in fuzzing performance.
mattweingarten/DAEM
Highly accurate Recommender Systems, including Collaborative Filtering, lie at the heart of a satisfactory customer experience and continuous user engagement for a plethora of large-scale online platforms. While Matrix Factorization is the most widely studied and applied Collaborative Filtering approach, there is evidence to suggest that linear techniques lack the complexity to sufficiently capture the underlying relationship between users and items. The use of neural networks like Autoencoders offers a potential remedy and may more accurately represent this relationship. In this work, we propose our Denoising Autoencoder Model (DÆM) for highly accurate Collaborative Filtering and show improvement over four evaluated state-of-the-art models.
mattweingarten/deloitte
mattweingarten/graal
GraalVM: Run Programs Faster Anywhere :rocket:
mattweingarten/lean4
Lean4 work in progress repo
mattweingarten/llvm-project
The LLVM Project is a collection of modular and reusable compiler and toolchain technologies.
mattweingarten/mattweingarten.github.io
mattweingarten/Mercury_XU5_PE1_Reference_Design
mattweingarten/mlprojects
Machine learning projects for Introductory class
mattweingarten/phase-1-checkpoint-challenge-3-teacher-refactoring
mattweingarten/polyglot_graal
mattweingarten/rails-blog
mattweingarten/react-js-tutorials
Code that goes along with my YouTube React JS Series
mattweingarten/react-rails
Following tutorial
mattweingarten/SinatraSkeleton
mattweingarten/twitter-clone
mattweingarten/Work_Station
mattweingarten/zynq-axi-dma-sg
Simple C snippet to transfer DMA memory with scatter/gather on a Zynq 7020