Viewing profile — liamdgray

Great idea. I would love to see your transpiler Mind sharing it?

Please do!

Some students even wish for a ban to reduce the pressure to keep up with social media. That reminded me of Warren Buffet asking for his kind and to be taxed more.

Abstract: "We introduce an Invertible Symbolic Regression (ISR) method. It is a machine learning technique that generates analytical relationships between inputs and outputs of a g…

Abstract: "Symbolic equations are at the core of scientific discovery. The task of discovering the underlying equation from a set of input-output pairs is called symbolic regressio…

Abstract: "In recent years, self-attention has become the dominant paradigm for sequence modeling in a variety of domains. However, in domains with very long sequence lengths the O…

Abstract: "Contemporary large models often exhibit behaviors suggesting the presence of low-level primitives that compose into modules with richer functionality, but these fundamen…

Abstract: "Current automated systems have crucial limitations that need to be addressed before artificial intelligence can reach human-like levels and bring new technological revol…

Abstract: "Associative Memories like the famous Hopfield Networks are elegant models for describing fully recurrent neural networks whose fundamental job is to store and retrieve i…

"Abstract A model of associative memory is studied, which stores and reliably retrieves many more patterns than the number of neurons in the network. We propose a simple duality be…

Is this intended to run on a quantum computer? You mention Grover's search algorithm, for example.

Recorded at the ML in PL 2019 Conference, the University of Warsaw, 22-24 November 2019. Anton Osokin (Higher School of Economics, Moscow), https://aosokin.github.io/ Slides availa…

Abstract Tomographic Synthetic Aperture Radar (TomoSAR) building object height inversion is a sparse reconstruction problem that utilizes the data obtained from several spacecraft …

Keywords: associative memory, Hopfield networks, transformers, attention, in-context learning, denoising Abstract: We introduce in-context denoising, a task that refines the connec…

Abstract: "Deep neural networks provide unprecedented performance gains in many real world problems in signal and image processing. Despite these gains, future development and prac…