Machine Learning

DePLM enhances protein optimization by denoising evolutionary information in protein language models via a rank-based diffusion process, improving mutation effect prediction and generalization.

GPR4DUR leverages Gaussian Process Regression to create density-based user representations for accurate multi-interest personalized retrieval, overcoming limitations of existing methods.

Boost associative memory capacity without extra parameters! DrDAM uses random features to approximate Dense Associative Memories, enabling efficient memory addition and retrieval.

Biologically-inspired Dit-CNNs leverage quadratic neuron integration to capture data correlation, achieving state-of-the-art performance on image classification benchmarks.

DEFT: A novel method efficiently fine-tunes diffusion models for conditional generation via a generalized h-transform, achieving state-of-the-art performance with significant speed improvements.

DeepLag improves fluid prediction by uniquely combining Lagrangian and Eulerian perspectives, tracking key particles to reveal hidden dynamics and improve prediction accuracy.

DeepITE: a novel variational graph autoencoder, efficiently estimates intervention targets from both labeled and unlabeled data, surpassing existing methods in recall and inference speed.

DeepDRK, a novel deep learning approach, significantly improves feature selection by effectively balancing false discovery rate and power, surpassing existing methods, especially with limited data.

Deep RL excels in simulated robotics, but struggles with real-world limitations like limited computational resources. This paper introduces Action Value Gradient (AVG), a novel incremental deep polic…

A simple, yet accurate model unveils deep learning’s mysteries, providing empirical insights into grokking, double descent, and gradient boosting, offering a new lens for analyzing neural network beha…

PSNR, a novel node-adaptive residual module, significantly improves deep GNN performance by mitigating over-smoothing and handling missing data.

Deep Graph Mating (GRAMA) enables training-free knowledge transfer in GNNs, achieving results comparable to pre-trained models without retraining or labeled data.

Deep Equilibrium Algorithmic Reasoners (DEARs) achieve superior performance on algorithmic tasks by directly solving for the equilibrium point of a neural network, eliminating the need for iterative r…

DECRL: A novel deep learning approach for temporal knowledge graph representation learning, capturing high-order correlation evolution and outperforming existing methods.

Decomposed Prompt Decision Transformer (DPDT) efficiently learns prompts for unseen tasks using a two-stage paradigm, achieving superior performance in multi-task offline reinforcement learning.

Decomposable Transformer Point Processes (DTPP) dramatically accelerates marked point process inference by using a mixture of log-normals for inter-event times and Transformers for marks, outperformin…

Decision Mamba-Hybrid (DM-H) accelerates in-context RL for long-term tasks by cleverly combining the strengths of Mamba’s linear long-term memory processing and transformer’s high-quality predictions,…

Decision Mamba: a novel offline RL model, leverages a multi-grained state space model and self-evolution regularization to overcome challenges with out-of-distribution data and noisy labels, achieving…

Decentralized noncooperative games with coupled decision-dependent distributions are analyzed, providing novel equilibrium concepts, uniqueness conditions, and a decentralized algorithm with sublinear…

DDN: Dual-domain Dynamic Normalization dynamically improves time series forecasting accuracy by addressing data distribution changes in both time and frequency domains via a plug-in module.