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algorithms
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attention
1
calibration
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cardinality
2
continual-learning
1
cpp
1
databases
2
decision-models
1
engineering
3
evaluation
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fine-tuning
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graph-neural-networks
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graphs
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imitation-learning
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knowledge-graphs
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language-models
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logic
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machine-learning
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memory
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open-source
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optimal-transport
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owl
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performance
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probability
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reinforcement-learning
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retrieval
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rust
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temporal-graphs
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algorithms
Competitive Programming with Rust: Invariants, Complexity, and a Reliable Workflow
2023-09-30
attention
BlockRank: Sparse Attention for Scalable In-Context Ranking
2026-03-01
calibration
Jev and Kev: Decision Models, Calibrated Probabilities, and Open-Source Deployment
2026-10-10
cardinality
Sampling-Based Cardinality Estimation: Unbiasedness, Consistency, and Complex Queries
2024-10-29
Characteristic Sets: When RDF Cardinality Is Exact, and When It Is Estimated
2024-02-10
continual-learning
Nested Learning: Memory, Optimization, and Multiple Timescales
2026-02-15
cpp
Performance Hints in C++: Models, Measurements, and Tradeoffs
2025-12-23
databases
Sampling-Based Cardinality Estimation: Unbiasedness, Consistency, and Complex Queries
2024-10-29
Characteristic Sets: When RDF Cardinality Is Exact, and When It Is Estimated
2024-02-10
decision-models
Jev and Kev: Decision Models, Calibrated Probabilities, and Open-Source Deployment
2026-10-10
engineering
Jev and Kev: Decision Models, Calibrated Probabilities, and Open-Source Deployment
2026-10-10
Performance Hints in C++: Models, Measurements, and Tradeoffs
2025-12-23
Competitive Programming with Rust: Invariants, Complexity, and a Reliable Workflow
2023-09-30
evaluation
Dynamic Link Prediction: Time, Negative Samples, and Honest Evaluation
2024-09-18
fine-tuning
RAFT: Learning to Answer with Retrieved Evidence
2024-07-20
graph-neural-networks
Reinforcement Learning on Graphs: States, Policies, and Evaluation
2024-03-18
graphs
Dynamic Link Prediction: Time, Negative Samples, and Honest Evaluation
2024-09-18
Reinforcement Learning on Graphs: States, Policies, and Evaluation
2024-03-18
imitation-learning
Primal Wasserstein Imitation Learning: Transport, Rewards, and Their Limits
2024-02-16
knowledge-graphs
Characteristic Sets: When RDF Cardinality Is Exact, and When It Is Estimated
2024-02-10
Handling owl:sameAs via Rewriting: Equality Without Fact Explosion
2024-02-05
language-models
RAFT: Learning to Answer with Retrieved Evidence
2024-07-20
logic
Handling owl:sameAs via Rewriting: Equality Without Fact Explosion
2024-02-05
machine-learning
Jev and Kev: Decision Models, Calibrated Probabilities, and Open-Source Deployment
2026-10-10
BlockRank: Sparse Attention for Scalable In-Context Ranking
2026-03-01
Nested Learning: Memory, Optimization, and Multiple Timescales
2026-02-15
memory
Nested Learning: Memory, Optimization, and Multiple Timescales
2026-02-15
open-source
Jev and Kev: Decision Models, Calibrated Probabilities, and Open-Source Deployment
2026-10-10
optimal-transport
Primal Wasserstein Imitation Learning: Transport, Rewards, and Their Limits
2024-02-16
owl
Handling owl:sameAs via Rewriting: Equality Without Fact Explosion
2024-02-05
performance
Performance Hints in C++: Models, Measurements, and Tradeoffs
2025-12-23
probability
Sampling-Based Cardinality Estimation: Unbiasedness, Consistency, and Complex Queries
2024-10-29
reinforcement-learning
Reinforcement Learning on Graphs: States, Policies, and Evaluation
2024-03-18
Primal Wasserstein Imitation Learning: Transport, Rewards, and Their Limits
2024-02-16
retrieval
BlockRank: Sparse Attention for Scalable In-Context Ranking
2026-03-01
RAFT: Learning to Answer with Retrieved Evidence
2024-07-20
rust
Competitive Programming with Rust: Invariants, Complexity, and a Reliable Workflow
2023-09-30
temporal-graphs
Dynamic Link Prediction: Time, Negative Samples, and Honest Evaluation
2024-09-18
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