#incremental learning try — diffusion models for protein design efficient attention beyond transformers papers like retrieval-augmented generation
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5 papers match
cs.SE 2026 ☆
LimICE: Integrating LLM into ICE Framework for Efficient Loop Invariant Inference Kai Fan, ShiWen Yu, GuangSheng Fan +3
The paper introduces LimICE, a tool that combines large language models with the ICE framework to incrementally generate sequences of lemmas as loop invariants, improving both succ…
#loop invariant synthesis #large language models #incremental learning #program verification
math.ST 2026 ☆
Compactly supported radial basis functions as probability density functions Sergio DÃaz-Elbal, Andrei MartÃnez-Finkelshtein, DarÃo Ramos-López
The paper proposes using compactly supported radial basis functions, especially Wendland C² kernels, as a new parametric family of probability density functions and develops method…
#radial basis functions #probability density estimation #mixture models #incremental learning
cs.CV 2026 ☆
Breaking the Model Forgetting Cycle in Long-Incremental 3D Object Detection Peisheng Qian, Jie Xu, Xulei Yang +1
The paper proposes a Learning‑Dynamics‑driven Memory and Review (LDMR) framework to reduce forgetting in long‑incremental 3D object detection by monitoring per‑class detection qual…
#incremental learning #3d object detection #model forgetting #memory bank
cs.CV 2026 ☆
Symbiosis-Inspired Knowledge Distillation for Incremental Object Detection Mingyue Zeng, De Cheng, Zhipeng Xu +3
The paper introduces Symbiosis-Inspired Knowledge Distillation (SIKD), a method for incremental object detection that leverages spatial and semantic relationships between old and n…
#incremental learning #object detection #knowledge distillation #symbiosis
cs.AI 2026 ☆
Mistake gating leads to energy and memory efficient continual learning Aaron Pache, Mark CW van Rossum
The paper introduces memorized mistake‑gated learning, a biologically inspired rule that updates neural network weights only when classification errors occur, cutting the number of…
#continual learning #online learning #incremental learning #energy efficiency
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