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Found 24 papers, 8 papers with code
The Next Decade of Telecommunications Artificial Intelligence
It has been an exciting journey since the mobile communications and artificial intelligence were conceived 37 years and 64 years ago.
Is Transfer Learning Necessary for Protein Landscape Prediction?
In this paper, we show that CNN models trained solely using supervised learning both compete with and sometimes outperform the best models from TAPE that leverage expensive pretraining on large protein datasets.
Fixed-Length Protein Embeddings using Contextual Lenses
We consider transformer (BERT) protein language models that are pretrained on the TrEMBL data set and learn fixed-length embeddings on top of them with contextual lenses.
Rethinking Attention with Performers
We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to quadratic) space and time complexity, without relying on any priors such as sparsity or low-rankness.
Ranked #7 on
Offline RL
on D4RL
Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers
In response, solutions that exploit the structure and sparsity of the learned attention matrix have blossomed.
Population-Based Black-Box Optimization for Biological Sequence Design
The cost and latency of wet-lab experiments requires methods that find good sequences in few experimental rounds of large batches of sequences--a setting that off-the-shelf black-box optimization methods are ill-equipped to handle.
Model-based reinforcement learning for biological sequence design
In response, we propose using reinforcement learning (RL) based on proximal-policy optimization (PPO) for biological sequence design.
Model-based Reinforcement Learning
reinforcement-learning
+1
Boundless: Generative Adversarial Networks for Image Extension
Image extension models have broad applications in image editing, computational photography and computer graphics.
Ranked #2 on
Uncropping
on Places2 val
Rapid Prediction of Electron-Ionization Mass Spectrometry using Neural Networks
When confronted with a substance of unknown identity, researchers often perform mass spectrometry on the sample and compare the observed spectrum to a library of previously-collected spectra to identify the molecule.
Learning Latent Permutations with Gumbel-Sinkhorn Networks
Permutations and matchings are core building blocks in a variety of latent variable models, as they allow us to align, canonicalize, and sort data.
Low-Rank Hidden State Embeddings for Viterbi Sequence Labeling
In textual information extraction and other sequence labeling tasks it is now common to use recurrent neural networks (such as LSTM) to form rich embedded representations of long-term input co-occurrence patterns.
End-to-End Learning for Structured Prediction Energy Networks
Structured Prediction Energy Networks (SPENs) are a simple, yet expressive family of structured prediction models (Belanger and McCallum, 2016).
Fast and Accurate Entity Recognition with Iterated Dilated Convolutions
Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs.
Ranked #24 on
Named Entity Recognition (NER)
on Ontonotes v5 (English)
Synthesizing Normalized Faces from Facial Identity Features
We present a method for synthesizing a frontal, neutral-expression image of a person's face given an input face photograph.
Ask the GRU: Multi-Task Learning for Deep Text Recommendations
In a variety of application domains the content to be recommended to users is associated with text.
Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks
Our goal is to combine the rich multistep inference of symbolic logical reasoning with the generalization capabilities of neural networks.
Structured Prediction Energy Networks
This deep architecture captures dependencies between labels that would lead to intractable graphical models, and performs structure learning by automatically learning discriminative features of the structured output.
Multilingual Relation Extraction using Compositional Universal Schema
In response, this paper introduces significant further improvements to the coverage and flexibility of universal schema relation extraction: predictions for entities unseen in training and multilingual transfer learning to domains with no annotation.
Bethe Learning of Conditional Random Fields via MAP Decoding
Many machine learning tasks can be formulated in terms of predicting structured outputs.
Bethe Projections for Non-Local Inference
Many inference problems in structured prediction are naturally solved by augmenting a tractable dependency structure with complex, non-local auxiliary objectives.
A Linear Dynamical System Model for Text
Finally, the Kalman filter updates can be seen as a linear recurrent neural network.
Learning Soft Linear Constraints with Application to Citation Field Extraction
Accurately segmenting a citation string into fields for authors, titles, etc.
MAP Inference in Chains using Column Generation
Linear chains and trees are basic building blocks in many applications of graphical models.
