Zero-shot Generalization
166 papers with code • 1 benchmarks • 1 datasets
Most implemented papers
Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Humans can understand and produce new utterances effortlessly, thanks to their compositional skills.
Multitask Prompted Training Enables Zero-Shot Task Generalization
Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020).
Learning Transferable Cooperative Behavior in Multi-Agent Teams
While multi-agent interactions can be naturally modeled as a graph, the environment has traditionally been considered as a black box.
Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset
In this work, we introduce the the Schema-Guided Dialogue (SGD) dataset, containing over 16k multi-domain conversations spanning 16 domains.
Learning the Travelling Salesperson Problem Requires Rethinking Generalization
End-to-end training of neural network solvers for graph combinatorial optimization problems such as the Travelling Salesperson Problem (TSP) have seen a surge of interest recently, but remain intractable and inefficient beyond graphs with few hundreds of nodes.
Convolutional Conditional Neural Processes
We introduce the Convolutional Conditional Neural Process (ConvCNP), a new member of the Neural Process family that models translation equivariance in the data.
Compositional Generalization with Tree Stack Memory Units
We study compositional generalization, viz., the problem of zero-shot generalization to novel compositions of concepts in a domain.
The Scattering Compositional Learner: Discovering Objects, Attributes, Relationships in Analogical Reasoning
In this work, we focus on an analogical reasoning task that contains rich compositional structures, Raven's Progressive Matrices (RPM).
From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models
To address this issue, we propose \emph{Img2Prompt}, a plug-and-play module that provides the prompts that can bridge the aforementioned modality and task disconnections, so that LLMs can perform zero-shot VQA tasks without end-to-end training.
ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth
Finally, ZoeD-M12-NK is the first model that can jointly train on multiple datasets (NYU Depth v2 and KITTI) without a significant drop in performance and achieve unprecedented zero-shot generalization performance to eight unseen datasets from both indoor and outdoor domains.