Search Results for author:
Found 23 papers, 10 papers with code
Enabling Massive Deep Neural Networks with the GraphBLAS
The performance of the GraphBLAS implementation is measured relative to a standard dense linear algebra library implementation.
Parallel Architecture and Hyperparameter Search via Successive Halving and Classification
We present a simple and powerful algorithm for parallel black box optimization called Successive Halving and Classification (SHAC).
Measuring Conversational Productivity in Child Forensic Interviews
Child Forensic Interviewing (FI) presents a challenge for effective information retrieval and decision making.
VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation
Generative models that can model and predict sequences of future events can, in principle, learn to capture complex real-world phenomena, such as physical interactions.
Ranked #15 on
Video Generation
on BAIR Robot Pushing
Learning Domain Invariant Representations for Child-Adult Classification from Speech
Diagnostic procedures for ASD (autism spectrum disorder) involve semi-naturalistic interactions between the child and a clinician.
Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum Eigengap
In this study, we propose a new spectral clustering framework that can auto-tune the parameters of the clustering algorithm in the context of speaker diarization.
Ranked #1 on
Speaker Diarization
on CALLHOME
(DER(ig olp) metric)
Designing Neural Speaker Embeddings with Meta Learning
Our experiments illustrate the applicability of meta-learning as a generalized learning paradigm for training deep neural speaker embeddings.
Audio and Speech Processing Sound
Necessary and Sufficient Condition for Satisfiability of a Boolean Formula in CNF and its Implications on P versus NP problem
Which leads to the necessary and sufficient condition for satisfiability of a boolean formula, in CNF.
Computational Complexity
ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification
We make use of a conditional generator for data augmentation that is trained directly using the meta-learning objective and simultaneously with prototypical networks, hence ensuring that data augmentation is customized to the task.
Colorization Transformer
We present the Colorization Transformer, a novel approach for diverse high fidelity image colorization based on self-attention.
Ranked #2 on
Colorization
on ImageNet val
Coexistence of coarsening and mean field relaxation in the long-range Ising chain
We study the kinetics after a low temperature quench of the one-dimensional Ising model with long range interactions between spins at distance $r$ decaying as $r^{-\alpha}$.
Statistical Mechanics
Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks
An emerging class of weather models based on neural networks represents a paradigm shift in weather forecasting: the models learn the required transformations from data instead of relying on hand-coded physics and are computationally efficient.
IISERB Brains at SemEval 2022 Task 6: A Deep-learning Framework to Identify Intended Sarcasm in English
This paper describes the system architectures and the models submitted by our team "IISERBBrains" to SemEval 2022 Task 6 competition.
Do better ImageNet classifiers assess perceptual similarity better?
Perceptual distances between images, as measured in the space of pre-trained deep features, have outperformed prior low-level, pixel-based metrics on assessing perceptual similarity.
Functional Optimization Reinforcement Learning for Real-Time Bidding
In order to evaluate the proposed RTB strategy's performance, we demonstrate the results on ten sequential simulated auction campaigns.
A Unified Framework for Optimization-Based Graph Coarsening
In this paper, we introduce a novel optimization-based framework for graph dimensionality reduction.
Large language models can segment narrative events similarly to humans
In the future, GPT-3 may thereby help to elucidate the principles underlying human event perception.
Dual PatchNorm
We propose Dual PatchNorm: two Layer Normalization layers (LayerNorms), before and after the patch embedding layer in Vision Transformers.
Scaling Vision Transformers to 22 Billion Parameters
The scaling of Transformers has driven breakthrough capabilities for language models.
Ranked #1 on
Zero-Shot Transfer Image Classification
on ObjectNet
Image Captioners Are Scalable Vision Learners Too
We further analyze the effect of the model architecture and scale, as well as the pretraining data on the representation quality, and find that captioning exhibits the same or better scaling behavior along these axes.
Frozen Feature Augmentation for Few-Shot Image Classification
Training a linear classifier or lightweight model on top of pretrained vision model outputs, so-called 'frozen features', leads to impressive performance on a number of downstream few-shot tasks.
IISERB Brains at SemEval-2022 Task 6: A Deep-learning Framework to Identify Intended Sarcasm in English
This paper describes the system architectures and the models submitted by our team “IISERB Brains” to SemEval 2022 Task 6 competition.
Controlled Data Generation via Insertion Operations for NLU
Use of synthetic data is rapidly emerging as a realistic alternative to manually annotating live traffic for industry-scale model building.
