Search Results for author:
Found 19 papers, 8 papers with code
FlatQuant: Flatness Matters for LLM Quantization
In this paper, we propose FlatQuant (Fast and Learnable Affine Transformation), a new post-training quantization approach to enhance flatness of weights and activations.
Hyperspectral Unmixing Under Endmember Variability: A Variational Inference Framework
This work proposes a variational inference (VI) framework for hyperspectral unmixing in the presence of endmember variability (HU-EV).
IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact
Such outliers are found to allocate most of the attention scores on initial tokens of input, termed as pivot tokens, which are crucial to the performance of quantized LLMs.
Wisdom of Committee: Distilling from Foundation Model to Specialized Application Model
Further, to accommodate the dissimilarity among the teachers in the committee, we introduce DiverseDistill, which allows the student to understand the expertise of each teacher and extract task knowledge.
Multilayer Simplex-structured Matrix Factorization for Hyperspectral Unmixing with Endmember Variability
Our multilayer model is based on the postulate that if we arrange the varied endmembers as an expanded endmember matrix, that matrix exhibits a low-rank structure.
Long-Term Value of Exploration: Measurements, Findings and Algorithms
We conduct live experiments on one of the largest short-form video recommendation platforms that serves billions of users to validate the new experiment designs, quantify the long-term values of exploration, and to verify the effectiveness of the adopted neural linear bandit algorithm for exploration.
Learning Disentangled Representations for Time Series
Motivated by the success of disentangled representation learning in computer vision, we study the possibility of learning semantic-rich time-series representations, which remains unexplored due to three main challenges: 1) sequential data structure introduces complex temporal correlations and makes the latent representations hard to interpret, 2) sequential models suffer from KL vanishing problem, and 3) interpretable semantic concepts for time-series often rely on multiple factors instead of individuals.
Probabilistic Simplex Component Analysis
PRISM uses a simple probabilistic model, namely, uniform simplex data distribution and additive Gaussian noise, and it carries out inference by maximum likelihood.
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning
Outlier detection is an important data mining task with numerous practical applications such as intrusion detection, credit card fraud detection, and video surveillance.
Mitigating Gender Bias in Captioning Systems
Image captioning has made substantial progress with huge supporting image collections sourced from the web.
Towards Deeper Graph Neural Networks with Differentiable Group Normalization
Graph neural networks (GNNs), which learn the representation of a node by aggregating its neighbors, have become an effective computational tool in downstream applications.
Dual Policy Distillation
In this work, we introduce dual policy distillation(DPD), a student-student framework in which two learners operate on the same environment to explore different perspectives of the environment and extract knowledge from each other to enhance their learning.
PyODDS: An End-to-end Outlier Detection System with Automated Machine Learning
Outlier detection is an important task for various data mining applications.
XDeep: An Interpretation Tool for Deep Neural Networks
XDeep is an open-source Python package developed to interpret deep models for both practitioners and researchers.
PyODDS: An End-to-End Outlier Detection System
PyODDS is an end-to end Python system for outlier detection with database support.
Towards Generalizable Deepfake Detection with Locality-aware AutoEncoder
The analysis further shows that LAE outperforms the state-of-the-arts by 6. 52%, 12. 03%, and 3. 08% respectively on three deepfake detection tasks in terms of generalization accuracy on previously unseen manipulations.
SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks
SpecAE leverages Laplacian sharpening to amplify the distances between representations of anomalies and the ones of the majority.
Deep Structured Cross-Modal Anomaly Detection
To this end, we propose a novel deep structured anomaly detection framework to identify the cross-modal anomalies embedded in the data.
Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding
Network embedding models are powerful tools in mapping nodes in a network into continuous vector-space representations in order to facilitate subsequent tasks such as classification and link prediction.
