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Found 16 papers, 3 papers with code
FERGI: Automatic Annotation of User Preferences for Text-to-Image Generation from Spontaneous Facial Expression Reaction
Researchers have proposed to use data of human preference feedback to fine-tune text-to-image generative models.
Bio-inspired learnable divisive normalization for ANNs
In this work we introduce DivNormEI, a novel bio-inspired convolutional network that performs divisive normalization, a canonical cortical computation, along with lateral inhibition and excitation that is tailored for integration into modern Artificial Neural Networks (ANNs).
Learning compact generalizable neural representations supporting perceptual grouping
Work at the intersection of vision science and deep learning is starting to explore the efficacy of deep convolutional networks (DCNs) and recurrent networks in solving perceptual grouping problems that underlie primate visual recognition and segmentation.
Deep Transfer Learning with Ridge Regression
The large amount of online data and vast array of computing resources enable current researchers in both industry and academia to employ the power of deep learning with neural networks.
Pain Evaluation in Video using Extended Multitask Learning from Multidimensional Measurements
Previous work on automated pain detection from facial expressions has primarily focused on frame-level pain metrics based on specific facial muscle activations, such as Prkachin and Solomon Pain Intensity (PSPI).
Ranked #1 on
Pain Intensity Regression
on UNBC-McMaster ShoulderPain dataset
(MAE (VAS) metric)
V1Net: A computational model of cortical horizontal connections
The primate visual system builds robust, multi-purpose representations of the external world in order to support several diverse downstream cortical processes.
Improving Sentence Representations with Multi-view Frameworks
Multi-view learning can provide self-supervision when different views are available of the same data.
A Simple Recurrent Unit with Reduced Tensor Product Representations
idely used recurrent units, including Long-short Term Memory (LSTM) and the Gated Recurrent Unit (GRU), perform well on natural language tasks, but their ability to learn structured representations is still questionable.
Improving Sentence Representations with Consensus Maximisation
Consensus maximisation learning can provide self-supervision when different views are available of the same data.
Exploiting Invertible Decoders for Unsupervised Sentence Representation Learning
The encoder-decoder models for unsupervised sentence representation learning tend to discard the decoder after being trained on a large unlabelled corpus, since only the encoder is needed to map the input sentence into a vector representation.
Multi-view Sentence Representation Learning
Multi-view learning can provide self-supervision when different views are available of the same data.
Exploring Asymmetric Encoder-Decoder Structure for Context-based Sentence Representation Learning
Context information plays an important role in human language understanding, and it is also useful for machines to learn vector representations of language.
Speeding up Context-based Sentence Representation Learning with Non-autoregressive Convolutional Decoding
We carefully designed experiments to show that neither an autoregressive decoder nor an RNN decoder is required.
Rethinking Skip-thought: A Neighborhood based Approach
We train our skip-thought neighbor model on a large corpus with continuous sentences, and then evaluate the trained model on 7 tasks, which include semantic relatedness, paraphrase detection, and classification benchmarks.
