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Found 13 papers, 3 papers with code
Comprehension Based Question Answering using Bloom’s Taxonomy
Current pre-trained language models have lots of knowledge, but a more limited ability to use that knowledge.
Unpacking Large Language Models with Conceptual Consistency
The success of Large Language Models (LLMs) indicates they are increasingly able to answer queries like these accurately, but that ability does not necessarily imply a general understanding of concepts relevant to the anchor query.
SAViR-T: Spatially Attentive Visual Reasoning with Transformers
We present a novel computational model, "SAViR-T", for the family of visual reasoning problems embodied in the Raven's Progressive Matrices (RPM).
Challenges in Procedural Multimodal Machine Comprehension:A Novel Way To Benchmark
We also observe a drop in performance across all the models when testing on RecipeQA and proposed Meta-RecipeQA (e. g. 83. 6% versus 67. 1% for HTRN), which shows that the proposed dataset is relatively less biased.
DAReN: A Collaborative Approach Towards Reasoning And Disentangling
Computational learning approaches to solving visual reasoning tests, such as Raven's Progressive Matrices (RPM), critically depend on the ability to identify the visual concepts used in the test (i. e., the representation) as well as the latent rules based on those concepts (i. e., the reasoning).
Comprehension Based Question Answering using Bloom's Taxonomy
Current pre-trained language models have lots of knowledge, but a more limited ability to use that knowledge.
Towards Solving Multimodal Comprehension
We then evaluate M3C using a textual cloze style question-answering task and highlight an inherent bias in the question answer generation method from [35] that enables a naive baseline to cheat by learning from only answer choices.
Zero-Shot Learning with Knowledge Enhanced Visual Semantic Embeddings
We improve zero-shot learning (ZSL) by incorporating common-sense knowledge in DNNs.
Task-Discriminative Domain Alignment for Unsupervised Domain Adaptation
In this paper, we improve the performance of DA by introducing a discriminative discrepancy measure which takes advantage of auxiliary information available in the source and the target domains to better align the source and target distributions.
Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement
We propose a family of novel hierarchical Bayesian deep auto-encoder models capable of identifying disentangled factors of variability in data.
Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach
The latter can be achieved by minimizing the maximum discrepancy of predictors (classifiers).
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Relevance Factor VAE: Learning and Identifying Disentangled Factors
We propose a novel VAE-based deep auto-encoder model that can learn disentangled latent representations in a fully unsupervised manner, endowed with the ability to identify all meaningful sources of variation and their cardinality.
Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach
Unsupervised domain adaptation (uDA) models focus on pairwise adaptation settings where there is a single, labeled, source and a single target domain.
