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Found 11 papers, 3 papers with code
Symmetry Adapted Residual Neural Network Diabatization: Conical Intersections in Aniline Photodissociation
We present a symmetry adapted residual neural network (SAResNet) diabatization method to construct quasi-diabatic Hamiltonians that accurately represent ab initio adiabatic energies, energy gradients, and nonadiabatic couplings for moderate sized systems.
Continual Learning of Nonlinear Independent Representations
Identifying the causal relations between interested variables plays a pivotal role in representation learning as it provides deep insights into the dataset.
On the Identification of Temporally Causal Representation with Instantaneous Dependence
To fill this gap, we propose an \textbf{ID}entification framework for instantane\textbf{O}us \textbf{L}atent dynamics (\textbf{IDOL}) by imposing a sparse influence constraint that the latent causal processes have sparse time-delayed and instantaneous relations.
Explainable Survival Analysis with Uncertainty using Convolution-Involved Vision Transformer
Highlights•Without patching WSIs, a novel ViT-based model is proposed for survival predictions.•We first introduce aleatoric uncertainty into the survival loss function.•We explain survival prediction using a post-hoc explainable method.•Our method outperforms baselines in accuracy, explainability, and reliability.
Practical Region-level Attack against Segment Anything Models
Segment Anything Models (SAM) have made significant advancements in image segmentation, allowing users to segment target portions of an image with a single click (i. e., user prompt).
Towards Adversarially Robust Dataset Distillation by Curvature Regularization
Extensive empirical experiments suggest that our method not only outperforms standard adversarial training on both accuracy and robustness with less computation overhead but is also capable of generating robust distilled datasets that can withstand various adversarial attacks.
When and How: Learning Identifiable Latent States for Nonstationary Time Series Forecasting
To solve this problem, we propose to learn IDentifiable latEnt stAtes (IDEA) to detect when the distribution shifts occur.
Computational Experiments Meet Large Language Model Based Agents: A Survey and Perspective
These agents, known as LLM-based Agent, offer the potential to enhance the anthropomorphism lacking in ABM.
CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process
Identifying the underlying time-delayed latent causal processes in sequential data is vital for grasping temporal dynamics and making downstream reasoning.
Revisiting Open World Object Detection
Open World Object Detection (OWOD), simulating the real dynamic world where knowledge grows continuously, attempts to detect both known and unknown classes and incrementally learn the identified unknown ones.
Self-learn to Explain Siamese Networks Robustly
Learning to compare two objects are essential in applications, such as digital forensics, face recognition, and brain network analysis, especially when labeled data is scarce and imbalanced.
