Publications (89)
Dimensionality-Aware Anomaly Detection in Learned Representations of Self-Supervised Speech Models
Sandra Arcos-Holzinger, Sarah M. Erfani, James Bailey +1
Self-supervised speech models (S3Ms) achieve strong downstream performance, yet their learned representations remain poorly understood under natural and adversarial perturbations.…
Coarse-to-Fine Open-Set Graph Node Classification with Large Language Models
Xueqi Ma, Xingjun Ma, Sarah Monazam Erfani +2
Developing open-set classification methods capable of classifying in-distribution (ID) data while detecting out-of-distribution (OOD) samples is essential for deploying graph neura…
Clean-Label Backdoor Attacks on Video Recognition Models
Shihao Zhao, Xingjun Ma, Xiang Zheng +3
Deep neural networks (DNNs) are vulnerable to backdoor attacks which can hide backdoor triggers in DNNs by poisoning training data. A backdoored model behaves normally on clean tes…
Adversarial Generation of Real-time Feedback with Neural Networks for Simulation-based Training
Xingjun Ma, Sudanthi Wijewickrema, Shuo Zhou +4
Simulation-based training (SBT) is gaining popularity as a low-cost and convenient training technique in a vast range of applications. However, for a SBT platform to be fully utili…
Attention in Space: Functional Roles of VLM Heads for Spatial Reasoning
Xueqi Ma, Shuo Yang, Yanbei Jiang +6
Despite remarkable advances in large Vision-Language Models (VLMs), spatial reasoning remains a persistent challenge. In this work, we investigate how attention heads within VLMs c…
On the Convergence and Robustness of Adversarial Training
Yisen Wang, Xingjun Ma, James Bailey +3
Improving the robustness of deep neural networks (DNNs) to adversarial examples is an important yet challenging problem for secure deep learning. Across existing defense techniques…
Normalized Loss Functions for Deep Learning with Noisy Labels
Xingjun Ma, Hanxun Huang, Yisen Wang +3
Robust loss functions are essential for training accurate deep neural networks (DNNs) in the presence of noisy (incorrect) labels. It has been shown that the commonly used Cross En…
E-BATS: Efficient Backpropagation-Free Test-Time Adaptation for Speech Foundation Models
Jiaheng Dong, Hong Jia, Soumyajit Chatterjee +3
Speech Foundation Models encounter significant performance degradation when deployed in real-world scenarios involving acoustic domain shifts, such as background noise and speaker…
Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression
Jiabo He, Sarah Erfani, Xingjun Ma +3
Bounding box (bbox) regression is a fundamental task in computer vision. So far, the most commonly used loss functions for bbox regression are the Intersection over Union (IoU) los…
Ground Truth Bias in External Cluster Validity Indices
Yang Lei, James C. Bezdek, Simone Romano +3
It has been noticed that some external CVIs exhibit a preferential bias towards a larger or smaller number of clusters which is monotonic (directly or inversely) in the number of c…
Dual Head Adversarial Training
Yujing Jiang, Xingjun Ma, Sarah Monazam Erfani +1
Deep neural networks (DNNs) are known to be vulnerable to adversarial examples/attacks, raising concerns about their reliability in safety-critical applications. A number of defens…
Invariant backpropagation: how to train a transformation-invariant neural network
Sergey Demyanov, James Bailey, Ramamohanarao Kotagiri +1
In many classification problems a classifier should be robust to small variations in the input vector. This is a desired property not only for particular transformations, such as t…
FCC-GAN: A Fully Connected and Convolutional Net Architecture for GANs
Sukarna Barua, Sarah Monazam Erfani, James Bailey
Generative Adversarial Networks (GANs) are a powerful class of generative models. Despite their successes, the most appropriate choice of a GAN network architecture is still not we…
Time Series Representation Learning with Supervised Contrastive Temporal Transformer
Yuansan Liu, Sudanthi Wijewickrema, Christofer Bester +2
Finding effective representations for time series data is a useful but challenging task. Several works utilize self-supervised or unsupervised learning methods to address this. How…
Semantic-Preserving Adversarial Text Attacks
Xinghao Yang, Weifeng Liu, James Bailey +2
Deep neural networks (DNNs) are known to be vulnerable to adversarial images, while their robustness in text classification is rarely studied. Several lines of text attack methods…
On local intrinsic dimensionality of deformation in complex materials
Shuo Zhou, Antoinette Tordesillas, Mehdi Pouragha +2
We propose a new metric called s-LID based on the concept of Local Intrinsic Dimensionality to identify and quantify hierarchies of kinematic patterns in heterogeneous media. s-LID…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1144
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP
Hanxun Huang, Sarah Erfani, Yige Li +2
As Contrastive Language-Image Pre-training (CLIP) models are increasingly adopted for diverse downstream tasks and integrated into large vision-language models (VLMs), their suscep…
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks
Hanxun Huang, Yisen Wang, Sarah Monazam Erfani +3
Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks. A range of defense methods have been proposed to train adversarially robust DNNs, among which adversa…
Neural Architecture Search via Combinatorial Multi-Armed Bandit
Hanxun Huang, Xingjun Ma, Sarah M. Erfani +1
Neural Architecture Search (NAS) has gained significant popularity as an effective tool for designing high performance deep neural networks (DNNs). NAS can be performed via policy…
What Do Deep Nets Learn? Class-wise Patterns Revealed in the Input Space
Shihao Zhao, Xingjun Ma, Yisen Wang +3
Deep neural networks (DNNs) are increasingly deployed in different applications to achieve state-of-the-art performance. However, they are often applied as a black box with limited…
LDReg: Local Dimensionality Regularized Self-Supervised Learning
Hanxun Huang, Ricardo J. G. B. Campello, Sarah Monazam Erfani +3
Representations learned via self-supervised learning (SSL) can be susceptible to dimensional collapse, where the learned representation subspace is of extremely low dimensionality…
Dimensionality-Aware Outlier Detection: Theoretical and Experimental Analysis
Alastair Anderberg, James Bailey, Ricardo J. G. B. Campello +4
We present a nonparametric method for outlier detection that takes full account of local variations in intrinsic dimensionality within the dataset. Using the theory of Local Intrin…
Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems
Xingjun Ma, Yuhao Niu, Lin Gu +4
Deep neural networks (DNNs) have become popular for medical image analysis tasks like cancer diagnosis and lesion detection. However, a recent study demonstrates that medical deep…
Stochastic Diffusion: A Diffusion Probabilistic Model for Stochastic Time Series Forecasting
Yuansan Liu, Sudanthi Wijewickrema, Dongting Hu +3
Recent innovations in diffusion probabilistic models have paved the way for significant progress in image, text and audio generation, leading to their applications in generative ti…
Efficient Orthogonal Parametrisation of Recurrent Neural Networks Using Householder Reflections
Zakaria Mhammedi, Andrew Hellicar, Ashfaqur Rahman +1
The problem of learning long-term dependencies in sequences using Recurrent Neural Networks (RNNs) is still a major challenge. Recent methods have been suggested to solve this prob…
End-to-End Anti-Backdoor Learning on Images and Time Series
Yujing Jiang, Xingjun Ma, Sarah Monazam Erfani +2
Backdoor attacks present a substantial security concern for deep learning models, especially those utilized in applications critical to safety and security. These attacks manipulat…
Backdoor Attacks on Time Series: A Generative Approach
Yujing Jiang, Xingjun Ma, Sarah Monazam Erfani +1
Backdoor attacks have emerged as one of the major security threats to deep learning models as they can easily control the model's test-time predictions by pre-injecting a backdoor…
Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition
Anqi Zhu, Qiuhong Ke, Mingming Gong +1
While remarkable progress has been made on supervised skeleton-based action recognition, the challenge of zero-shot recognition remains relatively unexplored. In this paper, we arg…
Sea ice floe segmentation in close-range optical imagery using active contour and foundation models
Giulio Passerotti, Alberto Alberello, Marcello Vichi +3
The size of sea ice floes in the marginal ice zone (MIZ) is a key factor influencing ice coverage, albedo, wave propagation, and ocean--atmosphere energy exchanges. Floe size can b…
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness
Xingjun Ma, Linxi Jiang, Hanxun Huang +3
Evaluating the robustness of a defense model is a challenging task in adversarial robustness research. Obfuscated gradients have previously been found to exist in many defense meth…
On the Use of Bagging for Local Intrinsic Dimensionality Estimation
Kristóf Péter, Ricardo J. G. B. Campello, James Bailey +1
The theory of Local Intrinsic Dimensionality (LID) has become a valuable tool for characterizing local complexity within and across data manifolds, supporting a range of data minin…
TopicResponse: A Marriage of Topic Modelling and Rasch Modelling for Automatic Measurement in MOOCs
Jiazhen He, Benjamin I. P. Rubinstein, James Bailey +2
This paper explores the suitability of using automatically discovered topics from MOOC discussion forums for modelling students' academic abilities. The Rasch model from psychometr…
GAVIN: Gaze-Assisted Voice-Based Implicit Note-taking
Anam Ahmad Khan, Joshua Newn, Ryan Kelly +3
Annotation is an effective reading strategy people often undertake while interacting with digital text. It involves highlighting pieces of text and making notes about them. Annotat…
Distilling Cognitive Backdoor Patterns within an Image
Hanxun Huang, Xingjun Ma, Sarah Erfani +1
This paper proposes a simple method to distill and detect backdoor patterns within an image: \emph{Cognitive Distillation} (CD). The idea is to extract the "minimal essence" from a…
Exploring Weak-to-Strong Generalization for CLIP-based Classification
Jinhao Li, Sarah M. Erfani, Lei Feng +2
Aligning large-scale commercial models with user intent is crucial to preventing harmful outputs. Current methods rely on human supervision but become impractical as model complexi…
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition
Anqi Zhu, Qiuhong Ke, Mingming Gong +1
Skeleton-based action recognition receives increasing attention because the skeleton representations reduce the amount of training data by eliminating visual information irrelevant…
Learning Deep Hidden Nonlinear Dynamics from Aggregate Data
Yisen Wang, Bo Dai, Lingkai Kong +3
Learning nonlinear dynamics from diffusion data is a challenging problem since the individuals observed may be different at different time points, generally following an aggregate…
Black-box Adversarial Attacks on Video Recognition Models
Linxi Jiang, Xingjun Ma, Shaoxiang Chen +2
Deep neural networks (DNNs) are known for their vulnerability to adversarial examples. These are examples that have undergone small, carefully crafted perturbations, and which can…
Learned Indexes with Distribution Smoothing via Virtual Points
Kasun Amarasinghe, Farhana Choudhury, Jianzhong Qi +1
Recent research on learned indexes has created a new perspective for indexes as models that map keys to their respective storage locations. These learned indexes are created to app…
Whose Side Are You On? Investigating the Political Stance of Large Language Models
Pagnarasmey Pit, Xingjun Ma, Mike Conway +6
Large Language Models (LLMs) have gained significant popularity for their application in various everyday tasks such as text generation, summarization, and information retrieval. A…
Investigating The Functional Roles of Attention Heads in Vision Language Models: Evidence for Reasoning Modules
Yanbei Jiang, Xueqi Ma, Shu Liu +5
Despite excelling on multimodal benchmarks, vision-language models (VLMs) largely remain a black box. In this paper, we propose a novel interpretability framework to systematically…
Emotion-Aware Quantization for Discrete Speech Representations: An Analysis of Emotion Preservation
Haoguang Zhou, Siyi Wang, Jingyao Wu +2
Modern speech systems increasingly use discretized self-supervised speech representations for compression and integration with token-based models, yet their impact on emotional inf…
Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions
Nodens Koren, Qiuhong Ke, Yisen Wang +2
Understanding the actions of both humans and artificial intelligence (AI) agents is important before modern AI systems can be fully integrated into our daily life. In this paper, w…
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses
Xiao Li, Xiang Zheng, Yifeng Gao +35
Embodied Artificial Intelligence (Embodied AI) integrates perception, cognition, planning, and interaction into agents that operate in open-world, safety-critical environments. As…
IGFormer: Interaction Graph Transformer for Skeleton-based Human Interaction Recognition
Yunsheng Pang, Qiuhong Ke, Hossein Rahmani +2
Human interaction recognition is very important in many applications. One crucial cue in recognizing an interaction is the interactive body parts. In this work, we propose a novel…
DDA: Dimensionality Driven Augmentation Search for Contrastive Learning in Laparoscopic Surgery
Yuning Zhou, Henry Badgery, Matthew Read +2
Self-supervised learning (SSL) has potential for effective representation learning in medical imaging, but the choice of data augmentation is critical and domain-specific. It remai…
A Time Decoupling Approach for Studying Forum Dynamics
Andrey Kan, Jeffrey Chan, Conor Hayes +3
Online forums are rich sources of information about user communication activity over time. Finding temporal patterns in online forum communication threads can advance our understan…
Finding Influentials in Twitter: A Temporal Influence Ranking Model
Xingjun Ma, Chunping Li, James Bailey +1
With the growing popularity of online social media, identifying influential users in these social networks has become very popular. Existing works have studied user attributes, net…
MOOCs Meet Measurement Theory: A Topic-Modelling Approach
Jiazhen He, Benjamin I. P. Rubinstein, James Bailey +3
This paper adapts topic models to the psychometric testing of MOOC students based on their online forum postings. Measurement theory from education and psychology provides statisti…
Providing Effective Real-time Feedback in Simulation-based Surgical Training
Xingjun Ma, Sudanthi Wijewickrema, Yun Zhou +3
Virtual reality simulation is becoming popular as a training platform in surgical education. However, one important aspect of simulation-based surgical training that has not receiv…
Dimensionality-Driven Learning with Noisy Labels
Xingjun Ma, Yisen Wang, Michael E. Houle +5
Datasets with significant proportions of noisy (incorrect) class labels present challenges for training accurate Deep Neural Networks (DNNs). We propose a new perspective for under…
Unlearnable Examples: Making Personal Data Unexploitable
Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani +2
The volume of "free" data on the internet has been key to the current success of deep learning. However, it also raises privacy concerns about the unauthorized exploitation of pers…
Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets
Dongxian Wu, Yisen Wang, Shu-Tao Xia +2
Skip connections are an essential component of current state-of-the-art deep neural networks (DNNs) such as ResNet, WideResNet, DenseNet, and ResNeXt. Despite their huge success in…
CoCoEmo: Composable and Controllable Human-Like Emotional TTS via Activation Steering
Siyi Wang, Shihong Tan, Siyi Liu +4
Emotional expression in human speech is nuanced and compositional, often involving multiple, sometimes conflicting, affective cues that may diverge from linguistic content. In cont…
Local Intrinsic Dimensionality of Ground Motion Data for Early Detection of Catastrophic Slope Failure
Yuansan Liu, James Bailey, Antoinette Tordesillas
Local Intrinsic Dimensionality (LID) has shown strong potential for anomaly detection in high-dimensional data, including landslide failure detection in granular media, where early…
Time-Transformer: Integrating Local and Global Features for Better Time Series Generation (Extended Version)
Yuansan Liu, Sudanthi Wijewickrema, Ang Li +3
Generating time series data is a promising approach to address data deficiency problems. However, it is also challenging due to the complex temporal properties of time series data,…
Adjusting for Chance Clustering Comparison Measures
Simone Romano, Nguyen Xuan Vinh, James Bailey +1
Adjusted for chance measures are widely used to compare partitions/clusterings of the same data set. In particular, the Adjusted Rand Index (ARI) based on pair-counting, and the Ad…
Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety
Xingjun Ma, Yifeng Gao, Yixu Wang +45
The rapid advancement of large models, driven by their exceptional abilities in learning and generalization through large-scale pre-training, has reshaped the landscape of Artifici…
Using voice note-taking to promote learners' conceptual understanding
Anam Ahmad Khan, Sadia Nawaz, Joshua Newn +3
Though recent technological advances have enabled note-taking through different modalities (e.g., keyboard, digital ink, voice), there is still a lack of understanding of the effec…
Symmetric Cross Entropy for Robust Learning with Noisy Labels
Yisen Wang, Xingjun Ma, Zaiyi Chen +3
Training accurate deep neural networks (DNNs) in the presence of noisy labels is an important and challenging task. Though a number of approaches have been proposed for learning wi…
Cognitive Mirrors: Exploring the Diverse Functional Roles of Attention Heads in LLM Reasoning
Xueqi Ma, Jun Wang, Yanbei Jiang +3
Large language models (LLMs) have achieved state-of-the-art performance in a variety of tasks, but remain largely opaque in terms of their internal mechanisms. Understanding these…
Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT
Miguel Ortiz del Castillo, Jonathan Morgan, Jack McRobbie +12
Artificial intelligence (AI) and autonomous edge computing in space are emerging areas of interest to augment capabilities of nanosatellites, where modern sensors generate orders o…
Bidirectional Adversarial Autoencoders for the design of Plasmonic Metasurfaces
Yuansan Liu, Jeygopi Panisilvam, Peter Dower +2
Deep Learning has been a critical part of designing inverse design methods that are computationally efficient and accurate. An example of this is the design of photonic metasurface…
Feedback Techniques in Computer-Based Simulation Training: A Survey
Sudanthi Wijewickrema, Xingjun Ma, James Bailey +2
Computer-based simulation training (CBST) is gaining popularity in a vast range of applications such as surgery, rehabilitation therapy, military applications, and driver/pilot tra…
Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles
Ranjie Duan, Xingjun Ma, Yisen Wang +3
Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. Existing works have mostly focused on either digital adversarial examples created via small and impe…
A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks
Zihan Yang, Richard O. Sinnott, James Bailey +1
In recent years, one of the most popular techniques in the computer vision community has been the deep learning technique. As a data-driven technique, deep model requires enormous…
RAIL: Rethinking Auditory Intelligence in Large Audio-Language Models with a CHC-Grounded Benchmark
Hongyu Jin, Siyi Wang, Yang Xiao +10
Humans process rich auditory environments through tightly integrated cognitive capabilities such as audio perception, audio reasoning, and memory. Despite recent progress in large…
PELP: Pioneer Event Log Prediction Using Sequence-to-Sequence Neural Networks
Wenjun Zhou, Artem Polyvyanyy, James Bailey
Process mining, a data-driven approach for analyzing, visualizing, and improving business processes using event logs, has emerged as a powerful technique in the field of business p…
DynaPURLS: Dynamic Refinement of Part-Aware Representations for Skeleton-Based Zero-Shot Action Recognition
Jingmin Zhu, Anqi Zhu, James Bailey +5
Zero-shot skeleton-based action recognition (ZS-SAR) is fundamentally constrained by prevailing approaches that rely on aligning skeleton features with static, class-level semantic…
Iterative Learning with Open-set Noisy Labels
Yisen Wang, Weiyang Liu, Xingjun Ma +4
Large-scale datasets possessing clean label annotations are crucial for training Convolutional Neural Networks (CNNs). However, labeling large-scale data can be very costly and err…
Unlearnable Examples For Time Series
Yujing Jiang, Xingjun Ma, Sarah Monazam Erfani +1
Unlearnable examples (UEs) refer to training samples modified to be unlearnable to Deep Neural Networks (DNNs). These examples are usually generated by adding error-minimizing nois…
Visual-Text Cross Alignment: Refining the Similarity Score in Vision-Language Models
Jinhao Li, Haopeng Li, Sarah Erfani +3
It has recently been discovered that using a pre-trained vision-language model (VLM), e.g., CLIP, to align a whole query image with several finer text descriptions generated by a l…
MurTree: Optimal Classification Trees via Dynamic Programming and Search
Emir DemiroviÄ, Anna Lukina, Emmanuel Hebrard +5
Decision tree learning is a widely used approach in machine learning, favoured in applications that require concise and interpretable models. Heuristic methods are traditionally us…
Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks
Yunfei Liu, Xingjun Ma, James Bailey +1
Recent studies have shown that DNNs can be compromised by backdoor attacks crafted at training time. A backdoor attack installs a backdoor into the victim model by injecting a back…
Online Cluster Validity Indices for Streaming Data
Masud Moshtaghi, James C. Bezdek, Sarah M. Erfani +2
Cluster analysis is used to explore structure in unlabeled data sets in a wide range of applications. An important part of cluster analysis is validating the quality of computation…
Divide and Learn: A Divide and Conquer Approach for Predict+Optimize
Ali Ugur Guler, Emir Demirovic, Jeffrey Chan +3
The predict+optimize problem combines machine learning ofproblem coefficients with a combinatorial optimization prob-lem that uses the predicted coefficients. While this problemcan…
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
Xingjun Ma, Bo Li, Yisen Wang +6
Deep Neural Networks (DNNs) have recently been shown to be vulnerable against adversarial examples, which are carefully crafted instances that can mislead DNNs to make errors durin…
A Framework to Adjust Dependency Measure Estimates for Chance
Simone Romano, Nguyen Xuan Vinh, James Bailey +1
Estimating the strength of dependency between two variables is fundamental for exploratory analysis and many other applications in data mining. For example: non-linear dependencies…
Reasoning Like Experts: Leveraging Multimodal Large Language Models for Drawing-based Psychoanalysis
Xueqi Ma, Yanbei Jiang, Sarah Erfani +4
Multimodal Large Language Models (MLLMs) have demonstrated exceptional performance across various objective multimodal perception tasks, yet their application to subjective, emotio…
Logical Queries over Views: Decidability and Expressiveness
James Bailey, Guozhu Dong, Anthony Widjaja To
We study the problem of deciding satisfiability of first order logic queries over views, our aim being to delimit the boundary between the decidable and the undecidable fragments o…
Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality
Sukarna Barua, Xingjun Ma, Sarah Monazam Erfani +2
Generative Adversarial Networks (GANs) are an elegant mechanism for data generation. However, a key challenge when using GANs is how to best measure their ability to generate reali…
Latent Video Prediction Learns Better World Models
Ali J Alrasheed, Aryan Yazdan Parast, Basim Azam +2
Self-supervised video models are increasingly framed as world models, yet their evaluation remains largely confined to a single top-1 accuracy score on clean benchmarks. This leave…
GeoDetect: Geometric Adversarial Detection for VLPs
Afsaneh Hasanebrahimi, Hanxun Huang, Christopher Leckie +2
The paper introduces GeoDetect, a method that uses geometric properties of vision‑language model embeddings to detect adversarial examples by measuring how far they deviate from th…
A Geometric Perspective on Composable Emotion Steering in Text-to-Speech Models
Siyi Wang, James Bailey, Ting Dang
While prior work has explored emotion control in hybrid text-to-speech systems, the geometric properties of these modules, and their implications for steerability, remain poorly un…
Spectral Flattening Is All Muon Needs: How Orthogonalization Controls Learning Rate and Convergence
Tien-Phat Nguyen, Truong Nguyen, Minh-Phuc Truong +3
Muon orthogonalizes the momentum buffer before each update, replacing its singular values with ones via Newton-Schulz iterations. This simple change lets Muon tolerate far larger l…
Parsimonious Dataset Construction for Laparoscopic Cholecystectomy Structure Segmentation
Yuning Zhou, Henry Badgery, Matthew Read +2
Labeling has always been expensive in the medical context, which has hindered related deep learning application. Our work introduces active learning in surgical video frame selecti…
Detecting Backdoor Samples in Contrastive Language Image Pretraining
Hanxun Huang, Sarah Erfani, Yige Li +2
Contrastive language-image pretraining (CLIP) has been found to be vulnerable to poisoning backdoor attacks where the adversary can achieve an almost perfect attack success rate on…
De Novo Molecular Generation with Stacked Adversarial Model
Yuansan Liu, James Bailey
Generating novel drug molecules with desired biological properties is a time consuming and complex task. Conditional generative adversarial models have recently been proposed as pr…