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Found 9 papers, 4 papers with code
TinyLlama: An Open-Source Small Language Model
We present TinyLlama, a compact 1. 1B language model pretrained on around 1 trillion tokens for approximately 3 epochs.
OtterHD: A High-Resolution Multi-modality Model
In this paper, we present OtterHD-8B, an innovative multimodal model evolved from Fuyu-8B, specifically engineered to interpret high-resolution visual inputs with granular precision.
Ranked #85 on
Visual Question Answering
on MM-Vet
One Network, Many Masks: Towards More Parameter-Efficient Transfer Learning
Fine-tuning pre-trained language models for multiple tasks tends to be expensive in terms of storage.
Lower Generalization Bounds for GD and SGD in Smooth Stochastic Convex Optimization
Our paper examines this observation by providing excess risk lower bounds for GD and SGD in two realizable settings: 1) $\eta T = \bigO{n}$, and (2) $\eta T = \bigOmega{n}$, where $n$ is the size of dataset.
Better Few-Shot Relation Extraction with Label Prompt Dropout
Our experiments show that our approach is able to lead to improved class representations, yielding significantly better results on the few-shot relation extraction task.
Sion's Minimax Theorem in Geodesic Metric Spaces and a Riemannian Extragradient Algorithm
Deciding whether saddle points exist or are approximable for nonconvex-nonconcave problems is usually intractable.
Rethinking the Variational Interpretation of Accelerated Optimization Methods
The continuous-time model of Nesterov's momentum provides a thought-provoking perspective for understanding the nature of the acceleration phenomenon in convex optimization.
Revisiting the Role of Euler Numerical Integration on Acceleration and Stability in Convex Optimization
Viewing optimization methods as numerical integrators for ordinary differential equations (ODEs) provides a thought-provoking modern framework for studying accelerated first-order optimizers.
Mixing of Stochastic Accelerated Gradient Descent
We study the mixing properties for stochastic accelerated gradient descent (SAGD) on least-squares regression.
