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Found 15 papers, 6 papers with code
LLM Task Interference: An Initial Study on the Impact of Task-Switch in Conversational History
With the recent emergence of powerful instruction-tuned large language models (LLMs), various helpful conversational Artificial Intelligence (AI) systems have been deployed across many applications.
ModEFormer: Modality-Preserving Embedding for Audio-Video Synchronization using Transformers
Lack of audio-video synchronization is a common problem during television broadcasts and video conferencing, leading to an unsatisfactory viewing experience.
GAMA: Generative Adversarial Multi-Object Scene Attacks
Using the joint image-text features to train the generator, we show that GAMA can craft potent transferable perturbations in order to fool victim classifiers in various attack settings.
UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images
Based on the outlined issues, we introduce a novel research problem of training CNN models for very large images, and present 'UltraMNIST dataset', a simple yet representative benchmark dataset for this task.
Poisson2Sparse: Self-Supervised Poisson Denoising From a Single Image
In this work, we explore a sparsity and dictionary learning-based approach and present a novel self-supervised learning method for single-image denoising where the noise is approximated as a Poisson process, requiring no clean ground-truth data.
A-ACT: Action Anticipation through Cycle Transformations
To incorporate this ability in intelligent systems a question worth pondering upon is how exactly do we anticipate?
Classification of histopathology images using ConvNets to detect Lupus Nephritis
Systemic lupus erythematosus (SLE) is an autoimmune disease in which the immune system of the patient starts attacking healthy tissues of the body.
APObind: A Dataset of Ligand Unbound Protein Conformations for Machine Learning Applications in De Novo Drug Design
Protein-ligand complex structures have been utilised to design benchmark machine learning methods that perform important tasks related to drug design such as receptor binding site detection, small molecule docking and binding affinity prediction.
DeepPocket: Ligand Binding Site Detection and Segmentation using 3D Convolutional Neural Networks
A structure-based drug design pipeline involves the development of potential drug molecules or ligands that form stable complexes with a given receptor at its binding site.
Ada-VSR: Adaptive Video Super-Resolution with Meta-Learning
Specifically, meta-learning is employed to obtain adaptive parameters, using a large-scale external dataset, that can adapt quickly to the novel condition (degradation model) of the given test video during the internal learning task, thereby exploiting external and internal information of a video for super-resolution.
Deep Quantized Representation for Enhanced Reconstruction
In this paper, we propose a data-driven Deep Quantized Latent Representation (DQLR) methodology for high-quality image reconstruction in the Shoot Apical Meristem (SAM) of Arabidopsis thaliana.
Optimal Sepsis Patient Treatment using Human-in-the-loop Artificial Intelligence
In this study, we develop a data-driven optimization solution that derives the optimal quantity of IV fluids for individual patients.
ALANET: Adaptive Latent Attention Network forJoint Video Deblurring and Interpolation
Different from these works, we address a more realistic problem of high frame-rate sharp video synthesis with no prior assumption that input is always blurry.
Adversarial Knowledge Transfer from Unlabeled Data
While machine learning approaches to visual recognition offer great promise, most of the existing methods rely heavily on the availability of large quantities of labeled training data.
Non-Adversarial Video Synthesis with Learned Priors
Different from these methods, we focus on the problem of generating videos from latent noise vectors, without any reference input frames.
