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Found 11 papers, 4 papers with code
Post-Hoc Reversal: Are We Selecting Models Prematurely?
This phenomenon is especially prominent in high-noise settings.
Turn Down the Noise: Leveraging Diffusion Models for Test-time Adaptation via Pseudo-label Ensembling
The goal of test-time adaptation is to adapt a source-pretrained model to a continuously changing target domain without relying on any source data.
Bridging the Gap: Exploring the Capabilities of Bridge-Architectures for Complex Visual Reasoning Tasks
In recent times there has been a surge of multi-modal architectures based on Large Language Models, which leverage the zero shot generation capabilities of LLMs and project image embeddings into the text space and then use the auto-regressive capacity to solve tasks such as VQA, captioning, and image retrieval.
Model-tuning Via Prompts Makes NLP Models Adversarially Robust
Across 5 NLP datasets, 4 adversarial attacks, and 3 different models, MVP improves performance against adversarial substitutions by an average of 8% over standard methods and even outperforms adversarial training-based state-of-art defenses by 3. 5%.
Generalization on Unseen Domains via Inference-Time Label-Preserving Target Projections
Generalization of machine learning models trained on a set of source domains on unseen target domains with different statistics, is a challenging problem.
Domain Generalization via Inference-time Label-Preserving Target Projections
Generalization of machine learning models trained on a set of source domains on unseen target domains with different statistics, is a challenging problem.
Learning Contextualized Knowledge Graph Structures for Commonsense Reasoning
Recently, neural-symbolic architectures have achieved success on commonsense reasoning through effectively encoding relational structures retrieved from external knowledge graphs (KGs) and obtained state-of-the-art results in tasks such as (commonsense) question answering and natural language inference.
Centralized active tracking of a Markov chain with unknown dynamics
A Lagrangian relaxation of the problem is solved by an artful blending of two tools: Gibbs sampling for MSE minimization and an on-line version of expectation maximization (EM) to estimate the unknown TPM.
Learning Contextualized Knowledge Structures for Commonsense Reasoning
Recently, knowledge graph (KG) augmented models have achieved noteworthy success on various commonsense reasoning tasks.
Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation
Knowledge graphs (KGs) have helped neural models improve performance on various knowledge-intensive tasks, like question answering and item recommendation.
Discrepancy Minimization in Domain Generalization with Generative Nearest Neighbors
Features extracted from this source domain are learned using a generative model whose latent space is used as a sampler to retrieve the nearest neighbors for the target data points.
