Search Results for author:
Found 15 papers, 7 papers with code
Submodular Trajectory Optimization for Aerial 3D Scanning
Drones equipped with cameras are emerging as a powerful tool for large-scale aerial 3D scanning, but existing automatic flight planners do not exploit all available information about the scene, and can therefore produce inaccurate and incomplete 3D models.
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process.
A High-Fidelity Open Embodied Avatar with Lip Syncing and Expression Capabilities
Embodied avatars as virtual agents have many applications and provide benefits over disembodied agents, allowing non-verbal social and interactional cues to be leveraged, in a similar manner to how humans interact with each other.
A System for Real-Time Interactive Analysis of Deep Learning Training
To achieve this, we model various exploratory inspection and diagnostic tasks for deep learning training processes as specifications for streams using a map-reduce paradigm with which many data scientists are already familiar.
Safe Reinforcement Learning via Curriculum Induction
In safety-critical applications, autonomous agents may need to learn in an environment where mistakes can be very costly.
An Empirical Analysis of Backward Compatibility in Machine Learning Systems
In many applications of machine learning (ML), updates are performed with the goal of enhancing model performance.
Understanding Failures of Deep Networks via Robust Feature Extraction
Traditional evaluation metrics for learned models that report aggregate scores over a test set are insufficient for surfacing important and informative patterns of failure over features and instances.
Ranking Architectures by Feature Extraction Capabilities
By training different architectures in the search space to the same training or validation error and subsequently comparing the usefulness of the features extracted on the task-dataset of interest by freezing most of the architecture we obtain quick estimates of the relative performance.
FEAR: A Simple Lightweight Method to Rank Architectures
We propose a simple but powerful method which we call FEAR, for ranking architectures in any search space.
Ranking Convolutional Architectures by their Feature Extraction Capabilities
We propose a simple but powerful method which we call FEAR, for ranking architectures in any search space.
LiteTransformerSearch: Training-free Neural Architecture Search for Efficient Language Models
Results show that the perplexity of 16-layer GPT-2 and Transformer-XL can be achieved with up to 1. 5x, 2. 5x faster runtime and 1. 2x, 2. 0x lower peak memory utilization.
One Network Doesn't Rule Them All: Moving Beyond Handcrafted Architectures in Self-Supervised Learning
We conduct a large-scale study with over 100 variants of ResNet and MobileNet architectures and evaluate them across 11 downstream scenarios in the SSL setting.
Small Character Models Match Large Word Models for Autocomplete Under Memory Constraints
In this work, we study the more challenging open-domain setting consisting of low frequency user prompt patterns (or broad prompts, e. g., prompt about 93rd academy awards) and demonstrate the effectiveness of character-based language models.
Textbooks Are All You Need
Despite this small scale, phi-1 attains pass@1 accuracy 50. 6% on HumanEval and 55. 5% on MBPP.
Ranked #41 on
Code Generation
on HumanEval
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
We introduce phi-3-mini, a 3. 8 billion parameter language model trained on 3. 3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3. 5 (e. g., phi-3-mini achieves 69% on MMLU and 8. 38 on MT-bench), despite being small enough to be deployed on a phone.
