Machine Learning

Research Overview 2025

Dr. Example Researcher

Agenda

Background & Motivation
Neural Network Fundamentals
Transformer Architecture
Experimental Results
Future Directions

The Deep Learning Revolution

graph TD A[Traditional ML] --> B[Deep Learning] B --> C[Computer Vision] B --> D[NLP] B --> E[Reinforcement Learning] C --> F[Multimodal AI] D --> F

Neural Network Basics

A neuron computes:

$$y = f\left(\sum_{i=1}^{n} w_i x_i + b\right)$$

Where:

$x_i$ = inputs
$w_i$ = weights
$b$ = bias
$f$ = activation function

Activation Functions

ReLU (Rectified Linear Unit):

$$f(x) = \max(0, x)$$

Sigmoid:

$$\sigma(x) = \frac{1}{1 + e^{-x}}$$

Softmax:

$$\text{softmax}(x_i) = \frac{e^{x_i}}{\sum_j e^{x_j}}$$

Transformer Architecture

Self-Attention Mechanism: $$\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V$$ Enables parallel processing of sequences

Note:

Q = Query matrix
K = Key matrix
V = Value matrix
d_k = dimension of keys

Model Architecture

class TransformerBlock(nn.Module):
    def __init__(self, d_model, n_heads):
        super().__init__()
        self.attention = MultiHeadAttention(d_model, n_heads)
        self.ffn = FeedForward(d_model)
        self.norm1 = LayerNorm(d_model)
        self.norm2 = LayerNorm(d_model)
    
    def forward(self, x):
        x = x + self.attention(self.norm1(x))
        x = x + self.ffn(self.norm2(x))
        return x

Experimental Setup

Dataset

Size: 1M samples
Split: 80/10/10
Preprocessing: Tokenization, normalization

Training

Optimizer: AdamW
Learning rate: 3e-4
Batch size: 256
Epochs: 100

Results

Model	Accuracy	F1 Score	Latency
Baseline	82.3%	0.81	45ms
Ours (small)	89.7%	0.88	38ms
Ours (large)	94.2%	0.93	52ms

+11.9% improvement over baseline

Ablation Study

Component	Accuracy Drop
w/o Attention	-8.3%
w/o FFN	-4.1%
w/o LayerNorm	-2.7%
w/o Residual	-6.5%

Self-attention is the most critical component

Loss Curve

The training converged smoothly:

$$\mathcal{L} = -\sum_{i=1}^{N} y_i \log(\hat{y}_i)$$ Cross-entropy loss with label smoothing (α=0.1)

Key Findings

Scaling matters: Larger models = better results
Attention is critical: Core to performance
Efficiency trade-offs: Accuracy vs. latency
Transfer learning: Pre-training helps

Limitations

Computational requirements
Data dependency
Interpretability challenges
Environmental impact

Future work should address these concerns

Future Directions

graph LR A[Current Work] --> B[Efficient Transformers] A --> C[Multimodal Learning] A --> D[Explainable AI] B --> E[Real-world Deployment] C --> E D --> E

References

Vaswani et al. (2017). Attention is All You Need
Brown et al. (2020). Language Models are Few-Shot Learners
Dosovitskiy et al. (2021). An Image is Worth 16x16 Words

Thank You

Questions?

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📄 Full paper:

Note: Contact information and resources for follow-up questions. Open for collaboration on future research directions.