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Found 7 papers, 4 papers with code
On Training Derivative-Constrained Neural Networks
We refer to the setting where the (partial) derivatives of a neural network's (NN's) predictions with respect to its inputs are used as additional training signal as a derivative-constrained (DC) NN.
ExpeL: LLM Agents Are Experiential Learners
The recent surge in research interest in applying large language models (LLMs) to decision-making tasks has flourished by leveraging the extensive world knowledge embedded in LLMs.
Push: Concurrent Probabilistic Programming for Bayesian Deep Learning
We introduce a library called Push that takes a probabilistic programming approach to Bayesian deep learning (BDL).
On Learning to Prove
In this paper, we consider the problem of learning a first-order theorem prover that uses a representation of beliefs in mathematical claims to construct proofs.
GamePad: A Learning Environment for Theorem Proving
In this paper, we introduce a system called GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant.
Augur: Data-Parallel Probabilistic Modeling
We show that the compiler can generate data-parallel inference code scalable to thousands of GPU cores by making use of the conditional independence relationships in the Bayesian network.
Augur: a Modeling Language for Data-Parallel Probabilistic Inference
In this paper, we present a probabilistic programming language and compiler for Bayesian networks designed to make effective use of data-parallel architectures such as GPUs.
