Search Results for author:
Found 16 papers, 6 papers with code
How Powerful Potential of Attention on Image Restoration?
Our designs provide a closer look at the attention mechanism and reveal that some simple operations can significantly affect the model performance.
SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization
Neural network pruning is a key technique towards engineering large yet scalable, interpretable, and generalizable models.
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions
In this paper, we study two natural loss functions for learning from aggregate responses: bag-level loss and the instance-level loss.
Greedy PIG: Adaptive Integrated Gradients
We demonstrate the success of Greedy PIG on a wide variety of tasks, including image feature attribution, graph compression/explanation, and post-hoc feature selection on tabular data.
Towards High-Quality Specular Highlight Removal by Leveraging Large-Scale Synthetic Data
This paper aims to remove specular highlights from a single object-level image.
Learning from Aggregated Data: Curated Bags versus Random Bags
Our method is based on the observation that the sum of the gradients of the loss function on individual data examples in a curated bag can be computed from the aggregate label without the need for individual labels.
Approximately Optimal Core Shapes for Tensor Decompositions
This work studies the combinatorial optimization problem of finding an optimal core tensor shape, also called multilinear rank, for a size-constrained Tucker decomposition.
Sequential Attention for Feature Selection
Feature selection is the problem of selecting a subset of features for a machine learning model that maximizes model quality subject to a budget constraint.
Deep Image-based Illumination Harmonization
Specifically, we firstly apply a physically-based rendering method to construct a large-scale, high-quality dataset (named IH) for our task, which contains various types of foreground objects and background scenes with different lighting conditions.
Feature Cross Search via Submodular Optimization
First, we show that it is not possible to provide an $n^{1/\log\log n}$-approximation algorithm for this problem unless the exponential time hypothesis fails.
A Multi-Task Network for Joint Specular Highlight Detection and Removal
Specular highlight detection and removal are fundamental and challenging tasks.
CRPN-SFNet: A High-Performance Object Detector on Large-Scale Remote Sensing Images
Extensive experiments on the public Dataset for Object deTection in Aerial images data set indicate that our CRPN can help our detector deal the larger image faster with the limited GPU memory; meanwhile, the SFNet is beneficial to achieve more accurate detection of geospatial objects with wide-scale range.
Learning to Detect Specular Highlights from Real-world Images
Specular highlight detection is a challenging problem, and has many applications such as shiny object detection and light source estimation.
edge2vec: Representation learning using edge semantics for biomedical knowledge discovery
We propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.
Deep & Cross Network for Ad Click Predictions
Feature engineering has been the key to the success of many prediction models.
On Reasoning with RDF Statements about Statements using Singleton Property Triples
If the singleton property triples describe a data triple, then how can a reasoner infer this data triple from the singleton property triples?
