Search Results for author:
Found 18 papers, 8 papers with code
Perspective-Equivariant Imaging: an Unsupervised Framework for Multispectral Pansharpening
Ill-posed image reconstruction problems appear in many scenarios such as remote sensing, where obtaining high quality images is crucial for environmental monitoring, disaster management and urban planning.
Representation Learning for Frequent Subgraph Mining
SPMiner combines graph neural networks, order embedding space, and an efficient search strategy to identify network subgraph patterns that appear most frequently in the target graph.
AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies
Our benchmarking approach focuses on aspects of this ability that are common among humans: (i) recalling related experiences from a large amount of information, and (ii) applying analogical reasoning to complex and lengthy scenarios.
Learning to Generate Text in Arbitrary Writing Styles
However, in applications such as writing assistants, it is desirable for language models to produce text in an author-specific style on the basis of a potentially small writing sample.
Ranking with Slot Constraints
We introduce the problem of ranking with slot constraints, which can be used to model a wide range of application problems -- from college admission with limited slots for different majors, to composing a stratified cohort of eligible participants in a medical trial.
Identifying the Risks of LM Agents with an LM-Emulated Sandbox
Alongside the emulator, we develop an LM-based automatic safety evaluator that examines agent failures and quantifies associated risks.
Can Authorship Representation Learning Capture Stylistic Features?
Automatically disentangling an author's style from the content of their writing is a longstanding and possibly insurmountable problem in computational linguistics.
Boosted Prompt Ensembles for Large Language Models
Methods such as chain-of-thought prompting and self-consistency have pushed the frontier of language model reasoning performance with no additional training.
Operator Splitting Value Iteration
We introduce new planning and reinforcement learning algorithms for discounted MDPs that utilize an approximate model of the environment to accelerate the convergence of the value function.
Learning to Imitate Object Interactions from Internet Videos
In this paper we make two main contributions: (1) a novel reconstruction technique RHOV (Reconstructing Hands and Objects from Videos), which reconstructs 4D trajectories of both the hand and the object using 2D image cues and temporal smoothness constraints; (2) a system for imitating object interactions in a physics simulator with reinforcement learning.
Material Prediction for Design Automation Using Graph Representation Learning
Successful material selection is critical in designing and manufacturing products for design automation.
Shadows Aren't So Dangerous After All: A Fast and Robust Defense Against Shadow-Based Adversarial Attacks
Robust classification is essential in tasks like autonomous vehicle sign recognition, where the downsides of misclassification can be grave.
SELFIES and the future of molecular string representations
We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.
Robust classification with flexible discriminant analysis in heterogeneous data
Linear and Quadratic Discriminant Analysis are well-known classical methods but can heavily suffer from non-Gaussian distributions and/or contaminated datasets, mainly because of the underlying Gaussian assumption that is not robust.
Simple Text Detoxification by Identifying a Linear Toxic Subspace in Language Model Embeddings
We hypothesize the existence of a low-dimensional toxic subspace in the latent space of pre-trained language models, the existence of which suggests that toxic features follow some underlying pattern and are thus removable.
Near Real-Time Social Distance Estimation in London
During the COVID-19 pandemic, policy makers at the Greater London Authority, the regional governance body of London, UK, are reliant upon prompt and accurate data sources.
Quantum Criticism: A Tagged News Corpus Analysed for Sentiment and Named Entities
In this research, we continuously collect data from the RSS feeds of traditional news sources.
Efficiently Exploring Ordering Problems through Conflict-directed Search
In this paper, we present Conflict-directed Incremental Total Ordering (CDITO), a conflict-directed search method to incrementally and systematically generate event total orders given ordering relations and conflicts returned by sub-solvers.
