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Found 15 papers, 6 papers with code
Interpretable Measures of Conceptual Similarity by Complexity-Constrained Descriptive Auto-Encoding
Quantifying the degree of similarity between images is a key copyright issue for image-based machine learning.
Meaning Representations from Trajectories in Autoregressive Models
We propose to extract meaning representations from autoregressive language models by considering the distribution of all possible trajectories extending an input text.
AugUndo: Scaling Up Augmentations for Unsupervised Depth Completion
The sparse depth modality have seen even less as intensity transformations alter the scale of the 3D scene, and geometric transformations may decimate the sparse points during resampling.
Sub-token ViT Embedding via Stochastic Resonance Transformers
We term our method ``Stochastic Resonance Transformer" (SRT), which we show can effectively super-resolve features of pre-trained ViTs, capturing more of the local fine-grained structures that might otherwise be neglected as a result of tokenization.
Tangent Transformers for Composition, Privacy and Removal
We introduce Tangent Attention Fine-Tuning (TAFT), a method for fine-tuning linearized transformers obtained by computing a First-order Taylor Expansion around a pre-trained initialization.
Tangent Model Composition for Ensembling and Continual Fine-tuning
Component models are composed at inference time via scalar combination, reducing the cost of ensembling to that of a single model.
Taming AI Bots: Controllability of Neural States in Large Language Models
We then characterize the subset of meanings that can be reached by the state of the LLMs for some input prompt, and show that a well-trained bot can reach any meaning albeit with small probability.
Train/Test-Time Adaptation with Retrieval
Second, we apply ${\rm T^3AR}$ for test-time adaptation and show that exploiting a pool of external images at test-time leads to more robust representations over existing methods on DomainNet-126 and VISDA-C, especially when few adaptation data are available (up to 8%).
Integral Continual Learning Along the Tangent Vector Field of Tasks
We propose a lightweight continual learning method which incorporates information from specialized datasets incrementally, by integrating it along the vector field of "generalist" models.
Not All Poisons are Created Equal: Robust Training against Data Poisoning
Data poisoning causes misclassification of test time target examples by injecting maliciously crafted samples in the training data.
Data-Efficient Augmentation for Training Neural Networks
To address this, we propose a rigorous technique to select subsets of data points that when augmented, closely capture the training dynamics of full data augmentation.
Friendly Noise against Adversarial Noise: A Powerful Defense against Data Poisoning Attacks
We make the key observation that attacks introduce local sharp regions of high training loss, which when minimized, results in learning the adversarial perturbations and makes the attack successful.
Monitored Distillation for Positive Congruent Depth Completion
In the absence of ground truth for model selection and training, our method, termed Monitored Distillation, allows a student to exploit a blind ensemble of teachers by selectively learning from predictions that best minimize the reconstruction error for a given image.
Stereoscopic Universal Perturbations across Different Architectures and Datasets
We study the effect of adversarial perturbations of images on deep stereo matching networks for the disparity estimation task.
Triplet Contrastive Learning for Brain Tumor Classification
Brain tumor is a common and fatal form of cancer which affects both adults and children.
