A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
PGD-trained models have a preferential direction in their gradients, which improves robustness.
Network alignment is a critical task to a wide variety of fields. Many existing works leverage on representation learning to accomplish this task without eliminating domain representation bias induced by domain-dependent features, which yield inferior alignment performance. This paper proposes a unified deep architectu…
For a standard convolutional neural network, optimizing over the input pixels to maximize the score of some target class will generally produce a grainy-looking version of the original image. However, Santurkar et al. (2019) demonstrated that for adversarially-trained neural networks, this optimization produces images …
Unified framework improves cross-corpus EEG emotion recognition by aligning prototypes and refining decision boundaries.
Paper proposes MDAT to stabilize domain alignment in label-scarce settings.
Paper proposes Adversarial CCA for multi-view alignment and generation.
ADGAN improves risk tolerance prediction by aligning cross-domain data.
This paper studies aligning knowledge graphs from different sources or languages. Most existing methods train supervised methods for the alignment, which usually require a large number of aligned knowledge triplets. However, such a large number of aligned knowledge triplets may not be available or are expensive to obta…
New method defends deep nets against large perturbations perceptible to humans.
AI models aligned with human vision perform well on few data tasks.
A Bayesian framework models adversarial uncertainty for robust machine learning.
A new method for aligning multiple distributions efficiently.
Proposes novel losses for fine-grained categorical domain adaptation.
High-dimensional neural network manifolds misalign with human perception, causing adversarial examples.
Proposes a non-adversarial method for distribution matching.
Shorter adversarial prompts help protect LLMs from jailbreak attacks.
Recent studies on the adversarial vulnerability of neural networks have shown that models trained to be more robust to adversarial attacks exhibit more interpretable saliency maps than their non-robust counterparts. We aim to quantify this behavior by considering the alignment between input image and saliency map. We h…
Adversarial training makes logistic regression weight loss landscapes sharper.
Graph-Relational Domain Adaptation (GRDA) adapts domains based on their graph structure.
Domain adaptation addresses the common problem when the target distribution generating our test data drifts from the source (training) distribution. While absent assumptions, domain adaptation is impossible, strict conditions, e.g. covariate or label shift, enable principled algorithms. Recently-proposed domain-adversa…
Unsupervised domain mapping has attracted substantial attention in recent years due to the success of models based on the cycle-consistency assumption. These models map between two domains by fooling a probabilistic discriminator, thereby matching the probability distributions of the real and generated data. Instead of…
We propose a method for unsupervised domain adaptation that trains a shared embedding to align the joint distributions of inputs (domain) and outputs (classes), making any classifier agnostic to the domain. Joint alignment ensures that not only the marginal distributions of the domain are aligned, but the labels as wel…
Mapping and translating professional but arcane clinical jargons to consumer language is essential to improve the patient-clinician communication. Researchers have used the existing biomedical ontologies and consumer health vocabulary dictionary to translate between the languages. However, such approaches are limited b…
The recent advances in deep transfer learning reveal that adversarial learning can be embedded into deep networks to learn more transferable features to reduce the distribution discrepancy between two domains. Existing adversarial domain adaptation methods either learn a single domain discriminator to align the global …
Gradient-trained shallow networks can generalize well but are vulnerable to small-radius adversarial attacks.
Adversarial training methods typically align distributions by solving two-player games. However, in most current formulations, even if the generator aligns perfectly with data, a sub-optimal discriminator can still drive the two apart. Absent additional regularization, the instability can manifest itself as a never-end…
Generative Adversarial Regression (GAR) learns risk scenarios robustly across policies.
Study reveals how neural network biases align with adversarial attack frequencies.
FRA-Attack improves adversarial transferability for closed-source MLLMs by aligning visual focus across models.
MetFA aligns source and target domains for cross-device image classification.
Adversarial training is a useful approach to promote the learning of transferable representations across the source and target domains, which has been widely applied for domain adaptation (DA) tasks based on deep neural networks. Until very recently, existing adversarial domain adaptation (ADA) methods ignore the usefu…
RePULSe improves language model alignment by reducing undesired outputs without sacrificing overall performance.
New method for robust distribution alignment using log-likelihood ratio and normalizing flows.
Many predicted structured objects (e.g., sequences, matchings, trees) are evaluated using the F-score, alignment error rate (AER), or other multivariate performance measures. Since inductively optimizing these measures using training data is typically computationally difficult, empirical risk minimization of surrogate …
This paper improves model robustness against adversarial attacks using optimal transport.
We study the question of how to imitate tasks across domains with discrepancies such as embodiment, viewpoint, and dynamics mismatch. Many prior works require paired, aligned demonstrations and an additional RL step that requires environment interactions. However, paired, aligned demonstrations are seldom obtainable an…
Adversarial training is a training scheme designed to counter adversarial attacks by augmenting the training dataset with adversarial examples. Surprisingly, several studies have observed that loss gradients from adversarially trained DNNs are visually more interpretable than those from standard DNNs. Although this phe…
We consider unsupervised domain adaptation: given labelled examples from a source domain and unlabelled examples from a related target domain, the goal is to infer the labels of target examples. Under the assumption that features from pre-trained deep neural networks are transferable across related domains, domain adap…
SONA improves conditional generation by balancing authenticity and alignment.
Aligns attention distributions for improved accuracy and robustness.
In this paper we propose a novel dual adversarial co-learning approach for multi-domain text classification (MDTC). The approach learns shared-private networks for feature extraction and deploys dual adversarial regularizations to align features across different domains and between labeled and unlabeled data simultaneo…
Paper tackles distribution matching by partially matching distributions, achieving robust results.
New method prevents adversarial training failure in deep networks.
Continuous appearance shifts such as changes in weather and lighting conditions can impact the performance of deployed machine learning models. While unsupervised domain adaptation aims to address this challenge, current approaches do not utilise the continuity of the occurring shifts. In particular, many robotics appl…
Neural networks have been proven to be vulnerable to a variety of adversarial attacks. From a safety perspective, highly sparse adversarial attacks are particularly dangerous. On the other hand the pixelwise perturbations of sparse attacks are typically large and thus can be potentially detected. We propose a new black…
ClusTR improves clustering-based models' robustness without adversarial training.
New method removes pseudo-label bias for unsupervised domain adaptation.
Label noise in adversarial training leads to robust overfitting, explained and mitigated.