SLASH: Probabilistic Circuits - Part 1
Explore probabilistic circuits in the SLASH framework. Learn their importance and applications in neuro-symbolic AI and machine learning.
SLASH Framework: Experiments - Part 3
Explore experiments in the SLASH framework for neuro-symbolic AI. Gain insights into cutting-edge research combining symbolic methods and deep learning.
Spatio-Temporal Perception Logic for Verification of Perceptual Systems - Part 1
Explore spatio-temporal perception logic for verifying perceptual systems in autonomous vehicles and other safety-critical applications.
Spatio-Temporal Perception Logic for Perceptual Systems Verification - Part 2
Explore spatio-temporal perception logic for verifying perceptual systems in autonomous vehicles, building on Part 1's foundations.
Diversity Measures: Domain-Independent Proxies for Failure in Language Model Queries
Explore domain-independent diversity measures to detect uncertainty in language model outputs, enhancing error detection and improving AI reliability.
Metacognition with EDCR: Error Detection and Correction in Neurosymbolic AI
Explore metacognition and EDCR, a neurosymbolic method for error detection in AI models. Learn about rule learning, intuitions, and applications in few-shot learning.
Spatio-Temporal Perception Logic for Perceptual Systems Verification - Part 3
Explore spatio-temporal perception logic for verifying perceptual systems in autonomous vehicles, building on previous parts of the series.
LangDiversity: Software to Identify LLM Errors - Diversity Measures and Prompt Selection
Explore LangDiversity software to identify LLM errors using diversity measures. Learn implementation, code examples, and prompt selection techniques for improving language model outputs.
Generative vs. Discriminative AI - A Comparative Analysis
Explore the key distinctions between generative and discriminative AI approaches, understanding their unique characteristics and applications.
Reinforcement Learning with PyReason as a Semantic Proxy
Explore PyReason as a semantic proxy for simulators in reinforcement learning, featuring a technical discussion on its application and recent research findings.
Google vs. Hallucinations in AI Overviews
Explore Google's challenges with LLM-based "AI Overviews" and the ongoing issue of hallucinations in AI products. Gain insights into the latest developments in neuro-symbolic AI and its potential impact on AGI.
Introduction to ML Ops and Continuous Delivery for Machine Learning
Explore ML operations and the Continuous Delivery for Machine Learning Model in this concise introduction to MLOps.
A* Search Algorithm: Introduction, Example, and Summary
Learn A* search algorithm: its principles, implementation, and applications in pathfinding and problem-solving. Explore this efficient heuristic search technique.
Decision Trees in Neuro-Symbolic AI
Master decision tree algorithms for effective data classification and prediction in machine learning applications.
Constraint Satisfaction Problem Variants in AI - Part 2
Explore constraint satisfaction problem variants, formulation techniques, solving methods, consistency algorithms, and domain splitting in AI.
Metacognitive AI: Recovering Constraints by Finding ML Errors
Explore metacognitive AI techniques for detecting and correcting ML model errors, focusing on constraint recovery in multi-class settings using neurosymbolic methods.
SAT Net: Learning MAXSAT Constraints - Part 1
Explore SAT Net's original work for learning MAXSAT constraints, presented by Paulo Shakarian in an ASU seminar on neurosymbolic reasoning.
Training LNNs via Primal Dual Methods - Part 2 of 3
Explore primal-dual methods for training Logical Neural Networks in neuro-symbolic reasoning, continuing the analysis of Lu et al.'s 2021 IBM research paper.
NeurASP: Experiments and Evaluation - Part 3
Explore experimental evaluations of NeurASP, a neuro-symbolic framework connecting ASP programs to neural architectures, enabling end-to-end training through gradient passage.
Deep Ontological Networks: Introduction - Part 1
Explore Deep Ontological Networks with Prof. Simari, covering framework introduction, Semantic Web concepts, and ontology applications in AI and machine learning.
Neural Networks, Temporal Logic, and Verification with STL Net - Part 2
Explore STL Net for specification checking of RNN and transformer outputs. Learn about architecture, loss functions, and practical applications in neural networks and temporal logic.