Search Results for author:
Found 17 papers, 5 papers with code
Baba Is AI: Break the Rules to Beat the Benchmark
Humans solve problems by following existing rules and procedures, and also by leaps of creativity to redefine those rules and objectives.
Revealing Vision-Language Integration in the Brain with Multimodal Networks
We operationalize sites of multimodal integration as regions where a multimodal vision-language model predicts recordings better than unimodal language, unimodal vision, or linearly-integrated language-vision models.
BrainBERT: Self-supervised representation learning for intracranial recordings
We create a reusable Transformer, BrainBERT, for intracranial recordings bringing modern representation learning approaches to neuroscience.
Continual Learning In Environments With Polynomial Mixing Times
The mixing time of the Markov chain induced by a policy limits performance in real-world continual learning scenarios.
On the Role of Weight Sharing During Deep Option Learning
In this work we note that while this key assumption of the policy gradient theorems of option-critic holds in the tabular case, it is always violated in practice for the deep function approximation setting.
Posing Fair Generalization Tasks for Natural Language Inference
Deep learning models for semantics are generally evaluated using naturalistic corpora.
Recursive Routing Networks: Learning to Compose Modules for Language Understanding
The model jointly optimizes the parameters of the functions and the meta-learner{'}s policy for routing inputs through those functions.
Routing Networks and the Challenges of Modular and Compositional Computation
Compositionality is a key strategy for addressing combinatorial complexity and the curse of dimensionality.
Stress-Testing Neural Models of Natural Language Inference with Multiply-Quantified Sentences
Standard evaluations of deep learning models for semantics using naturalistic corpora are limited in what they can tell us about the fidelity of the learned representations, because the corpora rarely come with good measures of semantic complexity.
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference
In this work we propose a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off between transfer and interference that can be optimized by enforcing gradient alignment across examples.
On the Effective Use of Pretraining for Natural Language Inference
Neural networks have excelled at many NLP tasks, but there remain open questions about the performance of pretrained distributed word representations and their interaction with weight initialization and other hyperparameters.
A Multi-Cultural Repository of Automatically Discovered Linguistic and Conceptual Metaphors
Researchers in the field can use this resource as a reference of typical metaphors used across these cultures.
Automatic Expansion of the MRC Psycholinguistic Database Imageability Ratings
Unfortunately, word imageability ratings were collected for only a limited number of words: 9, 240 words in English, 6, 233 in Spanish; and are unavailable at all in the other two languages studied: Russian and Farsi.
