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[Paper] Morphling: Fast, Fused, and Flexible GNN Training at Scale
Graph Neural Networks (GNNs) present a fundamental hardware challenge by fusing irregular, memory-bound graph traversals with regular, compute-intensive dense m...
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[Paper] In-context Inverse Optimality for Fair Digital Twins: A Preference-based approach
Digital Twins (DTs) are increasingly used as autonomous decision-makers in complex socio-technical systems. Their mathematically optimal decisions often diverge...
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Why AI Alignment Starts With Better Evaluation
You can’t align what you don’t evaluate The post Why AI Alignment Starts With Better Evaluation appeared first on Towards Data Science....
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[Paper] Delta Sum Learning: an approach for fast and global convergence in Gossip Learning
Federated Learning is a popular approach for distributed learning due to its security and computational benefits. With the advent of powerful devices in the net...
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[Paper] Teaching an Online Multi-Institutional Research Level Software Engineering Course with Industry - an Experience Report
Covid has made online teaching and learning acceptable and students, faculty, and industry professionals are all comfortable with this mode. This comfort can be...
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[Paper] MEGConformer: Conformer-Based MEG Decoder for Robust Speech and Phoneme Classification
We present Conformer-based decoders for the LibriBrain 2025 PNPL competition, targeting two foundational MEG tasks: Speech Detection and Phoneme Classification....
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[Paper] Tangram: Accelerating Serverless LLM Loading through GPU Memory Reuse and Affinity
Serverless Large Language Models (LLMs) have emerged as a cost-effective solution for deploying AI services by enabling a 'pay-as-you-go' pricing model through ...
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[Paper] Pascal-Weighted Genetic Algorithms: A Binomially-Structured Recombination Framework
This paper introduces a new family of multi-parent recombination operators for Genetic Algorithms (GAs), based on normalized Pascal (binomial) coefficients. Unl...
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[Paper] The Evolution of Learning Algorithms for Artificial Neural Networks
In this paper we investigate a neural network model in which weights between computational nodes are modified according to a local learning rule. To determine w...
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[Paper] Testing the Machine Consciousness Hypothesis
The Machine Consciousness Hypothesis states that consciousness is a substrate-free functional property of computational systems capable of second-order percepti...
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[Paper] Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI
Inference over large-scale foundation models within heterogeneous edge environments necessitates a fundamentally reconfigurable orchestration substrate. Static ...
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The Machine Learning and Deep Learning “Advent Calendar” Series: The Blueprint
Opening the black box of ML models, step by step, directly in Excel The post The Machine Learning and Deep Learning “Advent Calendar” Series: The Blueprint appe...