Search Results for author:
Found 16 papers, 7 papers with code
Multimodal Token Fusion for Vision Transformers
Many adaptations of transformers have emerged to address the single-modal vision tasks, where self-attention modules are stacked to handle input sources like images.
Ranked #1 on
Semantic Segmentation
on SUN-RGBD
(using extra training data)
CompanyKG: A Large-Scale Heterogeneous Graph for Company Similarity Quantification
To the best of our knowledge, CompanyKG is the first large-scale heterogeneous graph dataset originating from a real-world investment platform, tailored for quantifying inter-company similarity.
Simulation-Informed Revenue Extrapolation with Confidence Estimate for Scaleup Companies Using Scarce Time-Series Data
Investment professionals rely on extrapolating company revenue into the future (i. e. revenue forecast) to approximate the valuation of scaleups (private companies in a high-growth stage) and inform their investment decision.
PAUSE: Positive and Annealed Unlabeled Sentence Embedding
Sentence embedding refers to a set of effective and versatile techniques for converting raw text into numerical vector representations that can be used in a wide range of natural language processing (NLP) applications.
Simple, Scalable, and Stable Variational Deep Clustering
We then choose to focus on variational deep clustering (VDC) methods, since they mostly meet those criteria except for simplicity, scalability, and stability.
Prompt Tuned Embedding Classification for Multi-Label Industry Sector Allocation
All limitations (a), (b), and (c) are addressed by replacing the PLM's language head with a classification head, which is referred to as Prompt Tuned Embedding Classification (PTEC).
Bridged Transformer for Vision and Point Cloud 3D Object Detection
Recently, there is a trend of leveraging multiple sources of input data, such as complementing the 3D point cloud with 2D images that often have richer color and fewer noises.
Analyzing Linear Dynamical Systems: From Modeling to Coding and Learning
We then devise efficient algorithms to perform sparse coding and dictionary learning on the space of infinite-dimensional subspaces.
Sparse Coding and Dictionary Learning With Linear Dynamical Systems
To enhance the performance of LDSs, in this paper, we address the challenging issue of performing sparse coding on the space of LDSs, where both data and dictionary atoms are LDSs.
Pay Self-Attention to Audio-Visual Navigation
Audio-visual embodied navigation, as a hot research topic, aims training a robot to reach an audio target using egocentric visual (from the sensors mounted on the robot) and audio (emitted from the target) input.
Using Deep Learning to Find the Next Unicorn: A Practical Synthesis
The objective is a) to obtain a thorough and in-depth understanding of the methodologies for startup evaluation using DL, and b) to distil valuable and actionable learning for practitioners.
A Scalable and Adaptive System to Infer the Industry Sectors of Companies: Prompt + Model Tuning of Generative Language Models
The Private Equity (PE) firms operate investment funds by acquiring and managing companies to achieve a high return upon selling.
Sourcing Investment Targets for Venture and Growth Capital Using Multivariate Time Series Transformer
This paper addresses the growing application of data-driven approaches within the Private Equity (PE) industry, particularly in sourcing investment targets (i. e., companies) for Venture Capital (VC) and Growth Capital (GC).
Measuring Acoustics with Collaborative Multiple Agents
As humans, we hear sound every second of our life.
DISCOUNT: Distributional Counterfactual Explanation With Optimal Transport
This paper extends the concept of CEs to a distributional context, broadening the scope from individual data points to entire input and output distributions, named Distributional Counterfactual Explanation (DCE).
GenCeption: Evaluate Multimodal LLMs with Unlabeled Unimodal Data
We propose GenCeption, a novel and annotation-free MLLM evaluation framework that merely requires unimodal data to assess inter-modality semantic coherence and inversely reflects the models' inclination to hallucinate.
