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Found 10 papers, 8 papers with code
Holistic Evaluation of Language Models
We present Holistic Evaluation of Language Models (HELM) to improve the transparency of language models.
Generative Adversarial Transformers
We introduce the GANformer, a novel and efficient type of transformer, and explore it for the task of visual generative modeling.
Compositional Transformers for Scene Generation
We introduce the GANformer2 model, an iterative object-oriented transformer, explored for the task of generative modeling.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
Compositional Attention Networks for Machine Reasoning
We present the MAC network, a novel fully differentiable neural network architecture, designed to facilitate explicit and expressive reasoning.
Ranked #2 on
Visual Question Answering (VQA)
on CLEVR-Humans
Referring Expression Comprehension
Visual Question Answering (VQA)
+1
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering
We introduce GQA, a new dataset for real-world visual reasoning and compositional question answering, seeking to address key shortcomings of previous VQA datasets.
Ranked #6 on
Visual Question Answering (VQA)
on GQA test-std
Learning by Abstraction: The Neural State Machine
We introduce the Neural State Machine, seeking to bridge the gap between the neural and symbolic views of AI and integrate their complementary strengths for the task of visual reasoning.
Ranked #2 on
Visual Question Answering (VQA)
on GQA test-std
SODA: Bottleneck Diffusion Models for Representation Learning
We introduce SODA, a self-supervised diffusion model, designed for representation learning.
SLM: Learning a Discourse Language Representation with Sentence Unshuffling
We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation in a fully self-supervised manner.
Scaling Instructable Agents Across Many Simulated Worlds
Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI.
