Search Results for author:
Found 18 papers, 8 papers with code
Neural Spline Search for Quantile Probabilistic Modeling
Accurate estimation of output quantiles is crucial in many use cases, where it is desired to model the range of possibility.
The "Beatrix'' Resurrections: Robust Backdoor Detection via Gram Matrices
However, recent advanced backdoor attacks show that this assumption is no longer valid in dynamic backdoors where the triggers vary from input to input, thereby defeating the existing defenses.
M^4I: Multi-modal Models Membership Inference
To achieve this, we propose Multi-modal Models Membership Inference (M^4I) with two attack methods to infer the membership status, named metric-based (MB) M^4I and feature-based (FB) M^4I, respectively.
Does GNN Pretraining Help Molecular Representation?
Extracting informative representations of molecules using Graph neural networks (GNNs) is crucial in AI-driven drug discovery.
DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning
Continual learning aims to enable a single model to learn a sequence of tasks without catastrophic forgetting.
StyleFool: Fooling Video Classification Systems via Style Transfer
In this paper, we focus on unrestricted perturbations and propose StyleFool, a black-box video adversarial attack via style transfer to fool the video classification system.
PublicCheck: Public Integrity Verification for Services of Run-time Deep Models
Existing integrity verification approaches for deep models are designed for private verification (i. e., assuming the service provider is honest, with white-box access to model parameters).
Learning to Prompt for Continual Learning
The mainstream paradigm behind continual learning has been to adapt the model parameters to non-stationary data distributions, where catastrophic forgetting is the central challenge.
Towards understanding retrosynthesis by energy-based models
In this paper, we propose a framework that unifies sequence- and graph-based methods as energy-based models (EBMs) with different energy functions.
Measuring Vulnerabilities of Malware Detectors with Explainability-Guided Evasion Attacks
We find that (i) commercial antivirus engines are vulnerable to AMM-guided manipulated samples; (ii) the ability of a manipulated malware generated using one detector to evade detection by another detector (i. e., transferability) depends on the overlap of features with large AMM values between the different detectors; and (iii) AMM values effectively measure the importance of features and explain the ability to evade detection.
Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography
Data hiding is the process of embedding information into a noise-tolerant signal such as a piece of audio, video, or an image, including Digital Watermarking for robust identity verification and Steganography to embed data for the purpose of secure and secret communication.
TaskSet: A Dataset of Optimization Tasks
We present TaskSet, a dataset of tasks for use in training and evaluating optimizers.
Reverse engineering learned optimizers reveals known and novel mechanisms
Learned optimizers are algorithms that can themselves be trained to solve optimization problems.
Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics
Including prior knowledge is important for effective machine learning models in physics, and is usually achieved by explicitly adding loss terms or constraints on model architectures.
Energy-based View of Retrosynthesis
Retrosynthesis -- the process of identifying a set of reactants to synthesize a target molecule -- is of vital importance to material design and drug discovery.
Ranked #1 on
Single-step retrosynthesis
on USPTO-50k
Using a thousand optimization tasks to learn hyperparameter search strategies
We present TaskSet, a dataset of tasks for use in training and evaluating optimizers.
Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models
However, current sensors and imaging approaches still face significant limitations in SNR and sampling frequency; therefore statistical denoising and interpolation methods remain critical for understanding single-trial spatiotemporal dendritic voltage dynamics.
Scalable approximate Bayesian inference for particle tracking data
This approach is therefore highly flexible and improves on the state of the art in terms of accuracy; provides uncertainty estimates about the particle locations and identities; and has a test run-time that scales linearly as a function of the data length and number of particles, thus enabling Bayesian inference in arbitrarily large particle tracking datasets.
