Search Results for author:
Found 22 papers, 2 papers with code
Efficient AI in Practice: Training and Deployment of Efficient LLMs for Industry Applications
Large language models (LLMs) have demonstrated remarkable performance across a wide range of industrial applications, from search and recommendations to generative tasks.
360Brew: A Decoder-only Foundation Model for Personalized Ranking and Recommendation
In this report, we present our research to address these challenges by utilizing a large foundation model with a textual interface for ranking and recommendation tasks.
AlphaPO -- Reward shape matters for LLM alignment
In this paper, we argue that, for DAAs the reward (function) shape matters.
Efficient user history modeling with amortized inference for deep learning recommendation models
We study user history modeling via Transformer encoders in deep learning recommendation models (DLRM).
WxC-Bench: A Novel Dataset for Weather and Climate Downstream Tasks
WxC-Bench is designed as a dataset of datasets for developing ML-models for a complex weather and climate system, addressing selected downstream tasks as machine learning phenomenon.
DARD: A Multi-Agent Approach for Task-Oriented Dialog Systems
Task-oriented dialogue systems are essential for applications ranging from customer service to personal assistants and are widely used across various industries.
Prithvi WxC: Foundation Model for Weather and Climate
Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing number of large AI models that address use cases such as forecasting, downscaling, or nowcasting.
LiNR: Model Based Neural Retrieval on GPUs at LinkedIn
We envisage LiNR as a step towards integrating retrieval and ranking into a single GPU model, simplifying complex infrastructures and enabling end-to-end optimization of the entire differentiable infrastructure through gradient descent.
Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation
These parameterizations are subject to approximations and idealizations, which limit their capability and accuracy.
Neural Optimization with Adaptive Heuristics for Intelligent Marketing System
Computational marketing has become increasingly important in today's digital world, facing challenges such as massive heterogeneous data, multi-channel customer journeys, and limited marketing budgets.
LiMAML: Personalization of Deep Recommender Models via Meta Learning
In the realm of recommender systems, the ubiquitous adoption of deep neural networks has emerged as a dominant paradigm for modeling diverse business objectives.
LiRank: Industrial Large Scale Ranking Models at LinkedIn
We present LiRank, a large-scale ranking framework at LinkedIn that brings to production state-of-the-art modeling architectures and optimization methods.
A Precise Characterization of SGD Stability Using Loss Surface Geometry
In this paper, we delve deeper into the relationship between linear stability and sharpness.
MultiSlot ReRanker: A Generic Model-based Re-Ranking Framework in Recommendation Systems
In this paper, we propose a generic model-based re-ranking framework, MultiSlot ReRanker, which simultaneously optimizes relevance, diversity, and freshness.
FFSplit: Split Feed-Forward Network For Optimizing Accuracy-Efficiency Trade-off in Language Model Inference
This usually results in a trade-off between model accuracy and efficiency.
QuantEase: Optimization-based Quantization for Language Models
Particularly noteworthy is our outlier-aware algorithm's capability to achieve near or sub-3-bit quantization of LLMs with an acceptable drop in accuracy, obviating the need for non-uniform quantization or grouping techniques, improving upon methods such as SpQR by up to two times in terms of perplexity.
mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization
Modern deep learning models are over-parameterized, where different optima can result in widely varying generalization performance.
Improved Deep Neural Network Generalization Using m-Sharpness-Aware Minimization
To that end, this paper presents a thorough empirical evaluation of mSAM on various tasks and datasets.
Heterogeneous Calibration: A post-hoc model-agnostic framework for improved generalization
We introduce the notion of heterogeneous calibration that applies a post-hoc model-agnostic transformation to model outputs for improving AUC performance on binary classification tasks.
Logit Attenuating Weight Normalization
Over-parameterized deep networks trained using gradient-based optimizers are a popular choice for solving classification and ranking problems.
Transitioning from Real to Synthetic data: Quantifying the bias in model
This study aims to establish a trade-off between bias and fairness in the models trained using synthetic data.
Smoothed Gaussian Mixture Models for Video Classification and Recommendation
SGMM represents each video by the parameters of a Gaussian mixture model (GMM) trained for that video.
